Bone reconstruction and orthopedic implants

ABSTRACT

A method of constructing a patient-specific orthopedic implant comprising: (a) comparing a patient-specific abnormal bone model, derived from an actual anatomy of a patient&#39;s abnormal bone, with a reconstructed patient-specific bone model, also derived from the anatomy of the patient&#39;s bone, where the reconstructed patient-specific bone model reflects a normalized anatomy of the patient&#39;s bone, and where the patient-specific abnormal bone model reflects an actual anatomy of the patient&#39;s bone including at least one of a partial bone, a deformed bone, and a shattered bone, wherein the patient-specific abnormal bone model comprises at least one of a patient-specific abnormal point cloud and a patient-specific abnormal bone surface model, and wherein the reconstructed patient-specific bone model comprises at least one of a reconstructed patient-specific point cloud and a reconstructed patient-specific bone surface model; (b) optimizing one or more parameters for a patient-specific orthopedic implant to be mounted to the patient&#39;s abnormal bone using data output from comparing the patient-specific abnormal bone model to the reconstructed patient-specific bone model; and, (c) generating an electronic design file for the patient-specific orthopedic implant taking into account the one or more parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/891,047, entitled, “CRANIUM AND POSTCRANIAL BONEAND SOFT TISSUE RECONSTRUCTION,” filed Oct. 15, 2013, the disclosure ofwhich is incorporated herein by reference.

RELATED ART Field of the Invention

The present disclosure is directed to various aspects of orthopedicsincluding bone and tissue reconstruction, patient-specific and masscustomized orthopedic implants, gender and ethnic specific orthopedicimplants, cutting guides, trauma plates, bone graft cutting andplacement guides, patient-specific instruments, utilization of inertialmeasurement units for anatomical tracking for kinematics and pathology,and utilization of inertial measurement units for navigation duringorthopedic surgical procedures.

Introduction to the Invention

It is a first aspect of the present invention to provide a method ofconstructing a patient-specific orthopedic implant comprising: (a)comparing a patient-specific abnormal bone model, derived from an actualanatomy of a patient's abnormal bone, with a reconstructedpatient-specific bone model, also derived from the anatomy of thepatient's bone, where the reconstructed patient-specific bone modelreflects a normalized anatomy of the patient's bone, and where thepatient-specific abnormal bone model reflects an actual anatomy of thepatient's bone including at least one of a partial bone, a deformedbone, and a shattered bone, wherein the patient-specific abnormal bonemodel comprises at least one of a patient-specific abnormal point cloudand a patient-specific abnormal bone surface model, and wherein thereconstructed patient-specific bone model comprises at least one of areconstructed patient-specific point cloud and a reconstructedpatient-specific bone surface model; (b) optimizing one or moreparameters for a patient-specific orthopedic implant to be mounted tothe patient's abnormal bone using data output from comparing thepatient-specific abnormal bone model to the reconstructedpatient-specific bone model; and, (c) generating an electronic designfile for the patient-specific orthopedic implant taking into account theone or more parameters

In a more detailed embodiment of the first aspect, the method furtherincludes fabricating the patient-specific implant using the electronicdesign file. In yet another more detailed embodiment, the method furtherincludes comparing the patient-specific abnormal bone model to thereconstructed patient-specific bone model to identify missing bone ordeformed bone from the patient-specific abnormal bone model, andlocalizing the missing bone or deformed bone onto the reconstructedpatient-specific bone model. In a further detailed embodiment, themethod further includes generating the patient-specific abnormal bonemodel from data representative of the patient's abnormal bone, andgenerating the reconstructed patient-specific bone model from datarepresentative of the patient's abnormal bone and from data from astatistical atlas, where the statistical atlas data comprises at leastone of a point cloud and a surface model of a normal bone analogous tothe patient's abnormal bone. In still a further detailed embodiment, thedata representative of the patient's abnormal bone comprises at leastone of magnetic resonance images, computerized tomography images, X-rayimages, and ultrasound images. In a more detailed embodiment, thestatistical atlas data is derived from at least one of magneticresonance images, computerized tomography images, X-ray images, andultrasound images of the normal bone. In a more detailed embodiment, theidentified missing bone or the deformed bone comprises a set of boundingpoints, and localizing the missing bone or the deformed bone onto thereconstructed patient-specific bone model includes associating the setof bounding points with the reconstructed patient-specific bone model.In another more detailed embodiment, comparing the patient-specificabnormal bone model to the reconstructed patient-specific bone model toidentify missing bone or deformed bone from the patient-specificabnormal bone model includes outputting at least two lists of data,where the at least two lists of data include a first list identifyingthe missing bone or the deformed bone, and a second list identifyingbone in common between the patient-specific abnormal bone model and thereconstructed patient-specific bone model. In yet another more detailedembodiment, the first list comprises vertices belonging to the missingbone or the deformed bone from the patient-specific abnormal bone model,and the second list comprises vertices belong to bone in common betweenthe patient-specific abnormal bone model and the reconstructedpatient-specific bone model. In still another more detailed embodiment,the method further includes determining one or more patient-specificorthopedic implant fixation locations using data from thepatient-specific abnormal bone model and data from the reconstructedpatient-specific bone model.

In yet another more detailed embodiment of the first aspect, determiningone or more patient-specific orthopedic implant fixation locationsincludes excluding any location where the missing bone or the deformedbone has been identified. In yet another more detailed embodiment,optimizing one or more parameters for a patient-specific orthopedicimplant includes using an implant parameterizing template toestablishing general parameters that are thereafter optimized using thereconstructed patient-specific bone model. In a further detailedembodiment, the parameters include at least one of angle parameters,depth parameters, curvature parameters, and fixation device locationparameters. In still a further detailed embodiment, the method furthercomprises constructing an initial iteration of a surface model of thepatient-specific orthopedic implant. In a more detailed embodiment,constructing the initial iteration of the surface model includescombining contours from the patient-specific abnormal bone model andcontours from the reconstructed patient-specific bone model. In a moredetailed embodiment, constructing the initial iteration of the surfacemodel includes accounting for an intended implantation location for thepatient-specific orthopedic implant. In another more detailedembodiment, the method further includes constructing a subsequentiteration of the surface model of the patient-specific orthopedicimplant. In yet another more detailed embodiment, constructing thesubsequent iteration of the surface model of the patient-specificorthopedic implant includes a manual review of the subsequent iterationof the surface model and the reconstructed patient-specific bone modelto discern if a further iteration of the surface model is required. Instill another more detailed embodiment, the electronic design fileincludes at least one of a computer aided design file, a computernumerical control file, and a rapid manufacturing instruction file.

In a more detailed embodiment of the first aspect, the method furthercomprises generating an electronic design file for a patient-specificimplant placement guide using the one or more parameters optimized forthe patient-specific orthopedic implant. In yet another more detailedembodiment, the method further includes fabricating the patient-specificimplant placement guide using the electronic design file for thepatient-specific implant placement guide. In a further detailedembodiment, the one or more parameters optimized for thepatient-specific orthopedic implant includes at least one of a sizeparameter, a shape parameter, and a contour parameter. In still afurther detailed embodiment, at least one contour parameter is in commonamong the patient-specific orthopedic implant and the patient-specificimplant placement guide. In a more detailed embodiment, the methodfurther comprises designing a patient-specific implant placement guideto include a surface shape that is a negative of a surface shape of thepatient's bone where the patient-specific implant placement guide isintended to reside. In a more detailed embodiment, the patient-specificabnormal bone model comprises at least one of a patient-specificabnormal femur bone model and a patient-specific abnormal pelvis bonemodel derived from an actual anatomy of a patient's abnormal hip joint,the reconstructed patient-specific bone model comprises at least one ofa reconstructed patient-specific femur bone model and a reconstructedpatient-specific pelvis bone model derived from the anatomy of thepatient's hip joint, the reconstructed patient-specific model reflects anormalized anatomy from the patient's hip joint, and thepatient-specific abnormal bone model reflects an actual anatomy from thepatient's hip joint. In another more detailed embodiment, thepatient-specific abnormal bone model comprises the patient-specificabnormal femur bone model, the reconstructed patient-specific bone modelcomprises the reconstructed patient-specific femur bone model, thereconstructed patient-specific model reflects the normalized anatomyfrom a proximal femur of the patient, the patient-specific abnormal bonemodel reflects the actual anatomy from the proximal femur of thepatient, and the patient-specific orthopedic implant comprises a femoralstem implant.

In a more detailed embodiment of the first aspect, the patient-specificabnormal bone model comprises the patient-specific abnormal pelvis bonemodel, the reconstructed patient-specific bone model comprises thereconstructed patient-specific pelvis bone model, the reconstructedpatient-specific model reflects the normalized anatomy from thepatient's pelvis, the patient-specific abnormal bone model reflects theactual anatomy from the patient's pelvis, and the patient-specificorthopedic implant comprises an acetabular cup implant. In yet anothermore detailed embodiment, the electronic design file for thepatient-specific orthopedic implant includes at least one of a computeraided design file, a computer numerical control file, and a rapidmanufacturing instruction file.

It is a second aspect of the present invention to provide a method ofgenerating an electronic a reconstructed bone model of an abnormal bonecomprising: (a) utilizing at least one of a point cloud and a surfacemodel of an abnormal bone, where the abnormal bone includes at least oneof a partial bone, a deformed bone, and a shattered bone, for at leastone of identifying a bone from a statistical atlas that is similar tothe abnormal bone, registering a bone from a statistical atlas to theabnormal bone, and morphing surface points on a reconstructed model ofthe abnormal bone onto at least one of the point cloud and the surfacemodel of the abnormal bone; and, (b) generating the reconstructed modelof the abnormal bone.

In a more detailed embodiment of the second aspect, the step ofutilizing at least one of the point cloud and the surface model of anabnormal bone includes identifying the statistical atlas bone that ismost similar to the abnormal bone. In yet another more detailedembodiment, the step of utilizing at least one of the point cloud andthe surface model of an abnormal bone includes registering thestatistical atlas bone to the abnormal bone. In a further detailedembodiment, the step of utilizing at least one of the point cloud andthe surface model of an abnormal bone includes morphing surface pointson the reconstructed model of the abnormal bone onto at least one of thepoint cloud and the surface model of the abnormal bone. In still afurther detailed embodiment, identifying the statistical atlas bone thatis most similar to the abnormal bone includes using one or moresimilarity metrics to identify the statistical atlas bone. In a moredetailed embodiment, the statistical atlas includes a plurality ofmathematical representations, where each of the plurality ofmathematical representations is representative of a bone. In a moredetailed embodiment, the statistical atlas includes a plurality ofvirtual models, where each of the plurality of virtual models isrepresentative of a bone. In another more detailed embodiment, themethod further comprises registering at least one of the point cloud andthe surface model of the abnormal bone to an identified bone from thestatistical atlas that is similar to the abnormal bone. In yet anothermore detailed embodiment, the method further comprises enhancement ofshape parameters between (a) at least one of a point cloud and a surfacemodel of an abnormal bone, and (b) an identified bone from thestatistical atlas that is similar to the abnormal bone. In still anothermore detailed embodiment, enhancement of shape parameters includesinterpolating between (a) at least one of a point cloud and a surfacemodel of an abnormal bone, and (b) an identified bone from thestatistical atlas that is similar to the abnormal bone, in order toidentify missing bone or deformed bone in at least one of the pointcloud and the surface model of the abnormal bone.

In yet another more detailed embodiment of the second aspect,enhancement of the shape parameters results in generating surface pointscorresponding to the missing bone or deformed bone. In yet another moredetailed embodiment, the method further comprises morphing surfacepoints, having been interpolated from the bone from the statisticalatlas that is similar to the abnormal bone, with at least one of thepoint cloud and the surface model of the abnormal bone to generate thereconstructed model of the abnormal bone. In a further detailedembodiment, the abnormal bone comprises at least one of a deformedpelvis section, a shattered pelvis section, and a partial pelvis sectionmissing bone, and the reconstructed model of the abnormal bone comprisesat least a complete pelvis model section having remedied at least one ofa bone deformity in the deformed pelvis section, a shattered bonecomprising part of the shattered pelvis section, and a bone absence fromthe partial pelvis section. In still a further detailed embodiment, thecomplete pelvis model section includes an acetabular cup anatomy. In amore detailed embodiment, the abnormal bone comprises at least one of adeformed femur section, a shattered femur section, and a partial femursection missing bone, and the reconstructed model of the abnormal bonecomprises at least a complete femur model section having remedied atleast one of a bone deformity in the deformed femur section, a shatteredbone comprising part of the shattered femur section, and a bone absencefrom the partial femur section. In a more detailed embodiment, thecomplete femur model section comprises a proximal femur having neck andball anatomy. In another more detailed embodiment. In yet another moredetailed embodiment, the abnormal bone comprises at least one of adeformed humerus section, a shattered humerus section, and a partialhumerus section missing bone, a deformed ulna section, a shattered ulnasection, a partial ulna section missing bone, a deformed radius section,a shattered radius section, a partial radius section missing bone, adeformed cranium section, a shattered cranium section, a partial craniumsection missing bone, a deformed vertebra section, a shattered vertebrasection, and a partial vertebra section missing bone, and thereconstructed model of the abnormal bone comprises at least one of acomplete humerus model section, a complete ulna model section, acomplete radius model section a complete cranium model section, and acomplete vertebra model section having remedied at least one of a bonedeformity in the deformed ulna section, a shattered bone comprising partof the shattered ulna section, a bone absence from the partial ulnasection, a bone deformity in the deformed radius section, a shatteredbone comprising part of the shattered radius section, a bone absencefrom the partial radius section, a bone deformity in the deformedcranium section, a shattered bone comprising part of the shatteredcranium section, a bone absence from the partial cranium section, a bonedeformity in the deformed vertebra section, a shattered bone comprisingpart of the shattered vertebra section, and a bone absence from thepartial vertebra section.

It is a third aspect of the present invention to provide a method ofconstructing a mass-customized orthopedic implant comprising: (a)identifying features, where the features comprise at least one oflandmarks and shape features, across a statistical atlas population ofbones; (b) generating descriptors relevant to implant design using theidentified features across the statistical atlas population of bones;(c) grouping at least some of the descriptors into a group havingsimilar descriptors; (d) parameterizing the group to extract parametersfrom the group; and, (e) generating an electronic design file for amass-customized orthopedic implant.

In a more detailed embodiment of the third aspect, the method furtherincludes fabricating the mass-customized orthopedic implant using theelectronic design file. In yet another more detailed embodiment, theidentification of features step is automatically carried out by asoftware program configured to calculate landmarks across a statisticalatlas population of bones using location parameters embedded in acalculation logic. In a further detailed embodiment, the identificationof features step is automatically carried out by a software programconfigured to calculate shape features across a statistical atlaspopulation of bones using location parameters embedded in a calculationlogic. In still a further detailed embodiment, the descriptors comprisemathematical descriptors that are calculated across the statisticalatlas population of bones. In a more detailed embodiment, grouping atleast some of the descriptors into a group having similar descriptorsincludes using a statistical analysis to establish the group. In a moredetailed embodiment, the extracted descriptors from the group comprisedesign parameters for a shape of the mass-customized orthopedic implant.In another more detailed embodiment, the descriptors comprisemathematical descriptors, and parameterizing the group to extractdescriptors from the group includes converting the mathematicaldescriptors into surface descriptors. In yet another more detailedembodiment, the electronic design file for a mass-customized orthopedicimplant includes a virtual, three-dimensional model of themass-customized orthopedic implant. In still another more detailedembodiment, parameterizing the group to extract descriptors from thegroup includes generating a virtual, three-dimensional model of themass-customized orthopedic implant.

In yet another more detailed embodiment of the third aspect, the methodfurther includes extracting three-dimensional cancellous bone featuresacross the statistical atlas population of bones and generating a threedimensional bone model for each bone within the statistical atlaspopulation of bones that incorporates the extracted cancellous bonefeatures unique to that bone. In yet another more detailed embodiment,the method further includes conducting a porosity evaluation on eachbone within the statistical atlas population of bones to determinecancellous bone size and pore size. In a further detailed embodiment,the method further includes conducting stress testing process thatcombines cancellous bone size data, pore size data, and surfacedescriptor parameters to generate the electronic design file for themass-customized orthopedic implant. In still a further detailedembodiment, the electronic design file includes at least one of acomputer aided design file, a computer numerical control file, and arapid manufacturing instruction file. In a more detailed embodiment, themethod further includes generating an electronic design file for a masscustomized implant placement guide using at least one of the extractedparameters. In a more detailed embodiment, the method further includesfabricating the mass customized implant placement guide using theelectronic design file for the mass customized implant placement guide.In another more detailed embodiment, the statistical atlas population ofbones is ethnic specific. In yet another more detailed embodiment, thestatistical atlas population of bones is gender specific. In stillanother more detailed embodiment, the statistical atlas population ofbones comprises at least segments of femur bones. In yet another moredetailed embodiment, the statistical atlas population of bones comprisesat least segments of pelvis bones.

It is a fourth aspect of the present invention to provide a method ofconstructing a mass-customized trauma plate comprising: (a) establish avirtual boundary for a mass-customized trauma plate with respect to avirtual three dimensional bone model template; (b) select a plurality ofsurface points inside the virtual boundary corresponding to a surfacelocation on the virtual three dimensional bone model template; (c)propagating the plurality of surface points across a statistical atlascontaining a plurality of virtual three dimensional bone models; (d)using the plurality of surface points propagated onto each of theplurality of virtual three dimensional bone models to construct avirtual three dimensional bone plate fitted to that particular bonemodel; (e) extracting a plurality of curvatures representative of eachvirtual three dimensional bone plate created; (f) statistically analyzethe plurality of curvatures extracted to deduce shape parameters for themass-customized trauma plate; and, (g) generate an electronic designfile for the mass-customized trauma plate using the shape parameters.

It is a fifth aspect of the present invention to provide a method ofconstructing a patient-specific cutting guide for preparing a bone foran orthopedic implant comprising: (a) processing patient-specific bonecontours to determine a size of an orthopedic implant to be mounted tothe patient's bone and the location of the implant when mounted relativeto the patient's bone; (b) designing a patient-specific cutting guideusing the size of the orthopedic implant and the location the implantwhen mounted to the patient's bone; and, (c) fabricating a cutting guidethat is patient-specific that includes a shape that is a negative of theshape of the patient's bone to which the cutting guide is configured tobe mounted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an overall process of generating masscustomized and patient-specific molds from a partial anatomy.

FIG. 2 is a schematic diagram detailing how to add a new anatomicalstructure to a statistical atlas in order to generate correspondence.

FIG. 3 is a multi-resolution 3D registration algorithm overviewcorresponding to the multi-resolution 3D registration in FIG. 2.

FIG. 4 is a multi-scale registration of feature points using multi-scalefeatures.

FIG. 5 is a low level break down of multi-resolution registration asoutlined in FIG. 3.

FIG. 6 is a graphical representation of capturing variation inpopulation upon generation of correspondence

FIG. 7 is a schematic diagram of a full bone reconstruction processusing partial, deformed or shattered anatomy.

FIG. 8 is a schematic diagram of a defect classification process forgeneration of defect templates.

FIG. 9 is a graphical example of existing AAOS classifications foracetabular defects.

FIG. 10 is a graphical example of existing Paprosky acetabular defectclassification.

FIG. 11 is a three dimensional model representation of a patient withsevere pelvis discontinuity on the left. On the right is an example ofthe three dimensional model of the patient's pelvis shown on the left.

FIG. 12 is a comparison of the reconstructed left model and the originalpatient model, as well as right and left anatomy.

FIG. 13 is a distance map between a reconstructed model and a mirrorimage of the pelvis model reconstructed.

FIG. 14 is a patient with complete pelvis discontinuity and results ofreconstruction with rms error of 1.8 mm.

FIG. 15 are the results of reconstruction on partial skulls and meandistance map for reconstruction error.

FIG. 16 are the results of reconstruction of shattered femur.

FIG. 17 is a schematic diagram of the process of creating apatient-specific reconstructive implant.

FIG. 18 is a schematic diagram of the process for implant generationdepicted in FIG. 17.

FIG. 19 is a process flow diagram showing various steps forreconstruction of patient full anatomy from partial anatomy andgeneration of patient specific cup implant for pelvis discontinuity.

FIG. 20 is a graphical representation of a patient-specific placementguide for a patient-specific acetabular implant.

FIG. 21 comprises images studying the relationship between the threeattachment sites of an implant and the cup orientation for masscustomization.

FIG. 22 is a schematic diagram for a method for manufacturing a massproduced custom acetabular component using a modular design.

FIG. 23 is a schematic diagram of a process for generating apatient-specific hip stem for reconstructive surgeries.

FIG. 24 is a schematic diagram of a process for mass customized implantgeneration.

FIG. 25 is a schematic diagram depicting a process for using astatistical atlas for generation of both mass customized andpatient-specific hip implants.

FIG. 26 is a schematic diagram depicting a process for using astatistical atlas for generation of both mass customized andpatient-specific hip implant.

FIG. 27 is a schematic diagram depicting an outline of a process fordesigning population specific hip stem components.

FIG. 28 is a graphical representation showing where the proximal femurlandmarks are located.

FIG. 29 is a 3D model of a femur showing canal waist in the middle ofthe femur and femur waist along the length of the femur.

FIG. 30 is a graphical representation showing where the proximal femuraxes are located.

FIG. 31 is a graphical representation showing where the neck centercalculation is located.

FIG. 32 is a graphical representation of two points used to define afemur proximal anatomical axis.

FIG. 33 is a graphical representation of 3D proximal femur measurements.

FIG. 34 is shows an exemplary Dorr ratio, which is generally in 2D (fromXR).

FIG. 35 is a graphical representation of the B/A ratio at the IMIsthmus.

FIG. 36 is a graphical representation of IM canal measurements.

FIG. 37 is a contour and a fitted circle.

FIG. 38 is a graphical representation of the measurements taken toobtain the IM canal femur radii ratio.

FIG. 39 depicts two femur models showing the effect of the change in theradii ratio, with the one on the left having a radii ratio of 0.69, andthe one on the right having a radii ratio of 0.38.

FIG. 40 is a graphical representation of medial contours, neck axis andhead point of a proximal femur before alignment.

FIG. 41 is a graphical representation of an anatomical axis alignmentwith the Z-direction.

FIG. 42 is a graphical representation of medial contours aligned usingthe femoral neck pivot point.

FIG. 43 is a graphical representation of different models generatedusing interpolation between models to show the smoothness ofinterpolation.

FIG. 44 is a graphical and pictorial representation of three dimensionalmapping of bone density.

FIG. 45 is an X-ray depiction shown the IM width at 3 levels, and theproximal axis, head offset and femur head.

FIG. 46 is a plot of proximal angle versus head offset.

FIG. 47 is a plot of proximal angle versus head height.

FIG. 48 is a plot of head offset versus head height.

FIG. 49 is a proximal angle histogram.

FIG. 50 is a plot depicting clusters of females and males for headoffset and calcar diameter.

FIG. 51 is a plot depicting clusters of females and males for headoffset and proximal angle.

FIG. 52 is a head offset histogram.

FIG. 53 is an IM sizes histogram.

FIG. 54 is a graphical representation of female measurements withrespect to a proximal femur.

FIG. 55 is a graphical representation of male measurements with respectto a proximal femur.

FIG. 56 is a graphical representation of female measurements withrespect to the greater trochanter height.

FIG. 57 is a graphical representation of male measurements with respectto the greater trochanter height.

FIG. 58 IM canal shape difference between gender.

FIG. 59 Normal Female: T-score 1.1

FIG. 60 Osteopinia Female: T-score −1.3

FIG. 61 Osteoporosis Female: T-score −3

FIG. 62 Interpolated dataset headoffsets histogram.

FIG. 63 dataset Canal Sizes histogram.

FIG. 64 AP Head height measurement.

FIG. 65 Head Height Vs AP Head height relative to pivot point.

FIG. 66 Head Height Vs AP Head height relative to anatomical axismid-point.

FIG. 67 Parameters used for creation of hip stem implant family thataccommodates differences in both ethnicity and gender extracted fromclustering.

FIG. 68 Primary hip stem, assembled and exploded views.

FIG. 69 Revision hip stem, assembled and exploded views.

FIG. 70 Isolation of acetabular cup geometry.

FIG. 71 Acetabular cup anatomical templates.

FIG. 72 Anatomical acetabular cup and femoral stem ball shape exhibitingmultiple cup radii.

FIG. 73 Curvature matching between acetabular cup and femoral headcurvature affects kinematics and constraints.

FIG. 74 Contours defining cross sectional analysis of acetabular cup

FIG. 75 Transverse acetabular ligament automatically detected as methodfor cup orientation.

FIG. 76 Extracting porous shape and sizes to match bone anatomy fromMicro-CT.

FIG. 77 Pet specific implants and cutting guides.

FIG. 78 Mass customized orthopedic implants for pets using statisticalatlases.

FIG. 79 Process of generation of patient specific cutting and placementguides for hip system.

FIG. 80 Process of non-rigid registration for creation of patientspecific three dimensional pelvis and proximal femur models from x-ray.

FIG. 81 Multiple x-ray views used for reconstruction of pelvis andproximal femur.

FIG. 82 Automatic segmentation of pelvis and proximal femur from MRI andCT scans, as described in FIG. 79.

FIG. 83 Automatic segmentation of complex and shattered anatomy from MRIor CT, as outlined in FIG. 79.

FIG. 84 Process of virtual templating for both acetabular cup andfemoral stem components.

FIG. 85 Stem automatic placement using distal fixation.

FIG. 86 Stem automatic placement using press fit and three contacts.

FIG. 87 Automatic pelvis landmarking.

FIG. 88 Automatic calculation of cup orientation and placement.

FIG. 89 Cup and stem placement evaluation.

FIG. 90 Assessment of cup and stem placement to ensure overall limblength restoration and orientation.

FIG. 91 Preplanning interface for evaluating and modifying implantplacement and sizing.

FIG. 92 Process of using patient specific guide for resection andplacement of femoral stem.

FIG. 93 Process of using patient specific guide for reaming andplacement of acetabular cup.

FIG. 94 Mapping of patient specific labrum attachment site, in thisexample the acetabulum, which is used for generation of patient specificguide and locking mechanism. A statistical atlas, or templates, can beused to determine patient specific guide mating sites.

FIG. 95 Process of creating trauma plates and fixation devices for apopulation.

FIG. 96 Localization of plate shape on atlas mean bone.

FIG. 97 Propagation of plate loci on entire population, here shown on asingle instance.

FIG. 98 Extraction of plate midline curve.

FIG. 99 Computing 3D radii of curvature for plate midline curve.

FIG. 100 Calculating plate length.

FIG. 101 Calculating mid-plate width.

FIG. 102 Calculating plate cross sectional radii.

FIG. 103 Determining optimal number of clusters.

FIG. 104 Plate sizes clustering. Shown in FIG. 95 as “Clustering”.

FIG. 105 Parameterization of plate sizes. Shown in FIG. 95 as“Parameterized Curves” and “Generate Models”.

FIG. 106 Fitting generated plate on population for evaluation.

FIG. 107 3D surface distance map between plate surface and bone forevaluating plate fit.

FIG. 108 Validation of designed plate on cadaver to avoid muscle andligament impingement.

FIG. 109 Identifying Clavicle Midline Curvature. The Midline curvatureis not symmetrically “S” shaped, according to a statistical analysis ofthe anatomical population.

FIG. 110 Superior lateral plate (left), plate midline curve (center) andmidline plate curvature showing radius of curvature (right).

FIG. 111 Anterior mid-shaft 7 h plate (left), plate midline curve(center) and midline plate curvature showing single radius of curvature(right).

FIG. 112 Superior mid-shaft plate (left), plate midline curve (center)and midline plate curvature showing differing radii of curvature(right).

FIG. 113 Anterior lateral plate (left), plate midline curve (center) andmidline plate curvature showing differing radii of curvature (right).

FIG. 114 Anterior mid-shaft long plate (left), plate midline curve(center) and midline plate curvature showing differing radii ofcurvature (right).

FIG. 115 Process of generating customized plate placement guides fortrauma reconstructive surgeries.

FIG. 116 A process of generating customized cutting and placement guidefor reconstructive surgeries using bone grafts.

FIG. 117 A diagram depicting points of interest and eventual mounting ofa customized implant.

DETAILED DESCRIPTION

The exemplary embodiments of the present disclosure are described andillustrated below to encompass various aspects of orthopedics includingbone and tissue reconstruction, patient-specific and mass customizedorthopedic implants, gender and ethnic specific orthopedic implants,cutting guides, trauma plates, bone graft cutting and placement guides,and patient-specific instruments. Of course, it will be apparent tothose of ordinary skill in the art that the embodiments discussed beloware exemplary in nature and may be reconfigured without departing fromthe scope and spirit of the present invention. However, for clarity andprecision, the exemplary embodiments as discussed below may includeoptional steps, methods, and features that one of ordinary skill shouldrecognize as not being a requisite to fall within the scope of thepresent invention.

Full Anatomy Reconstruction

Referring to FIGS. 1-8, reconstruction of a deformed anatomy or apartial anatomy is one of the complex problems facing healthcareproviders. Loss of anatomy may be the result of birth conditions,tumors, diseases, personal injuries, or failure of previous surgeries.As part of providing treatment for various ailments, healthcareproviders may find it advantageous to reconstruct an anatomy orconstruct an anatomy to facilitate treatment for various conditions thatmay include, without limitation, broken/shattered bones, bonedegeneration, orthopedic implant revision, joint degeneration, andcustom instrumentation design. For example, prior art hip reconstructionsolution requires mirroring of the healthy patient anatomy which may notbe an accurate reflection of the healthy anatomy due to naturallyoccurring asymmetry, as shown in FIGS. 12-16.

The present disclosure provides a system and methods for bone and tissuereconstruction. In order to carry out this reconstruction, the systemand associated methods utilizes anatomical images representative of oneor more persons. These images are processed to create a virtual threedimensional (3D) tissue model or a series of virtual 3D tissue modelsmimicking the proper anatomy in question. Thereafter, the system andassociated methods are utilized to create a mold and/or other devices(e.g., fixation devices, grafting devices, patient-specific implants,patient-specific surgical guides) for use with reconstructive surgery.

As represented in FIG. 1, an overview of the exemplary system flowbegins with receiving input data representative of an anatomy. Thisanatomy may comprise a partial anatomy in the case of tissuedegeneration or tissue absence resulting from genetics, or this anatomymay comprise a deformed anatomy resulting from genetics or environmentalconditions, or this anatomy may comprise a shattered tissue resultingfrom one or more anatomy breaks. Input anatomical data comprises twodimensional (2D) images or three dimensional (3D) surfacerepresentations of the anatomy in question that may, for example, be inthe form of a surface model or point cloud. In circumstances where 2Dimages are utilized, these 2D images are utilized to construct a 3Dvirtual surface representation of the anatomy in question. Those skilledin the art are familiar with utilizing 2D images of anatomy to constructa 3D surface representation. Accordingly, a detailed explanation of thisprocess has been omitted in furtherance of brevity. By way of example,input anatomical data may comprise one or more of X-rays, computedtomography (CT) scans, magnetic resonance images (MRIs), or any otherimaging data from which a 3D surface representation of the tissue inquestion may be generated.

Referring to FIG. 45 and Table I, in the context of X-ray images used toconstruct a virtual 3D bone model, it has been discovered that bonerotation during imaging plays an important role in correctlyconstructing the model. In other words, if one attempts to compile X-rayimages in circumstances where bone rotation has occurred between images,the X-ray images need to be normalized to account for this bonerotation.

By way of example, in the context of a proximal femur, it has beendiscovered that bone rotation of six and fifteen degrees results insignificant changes to the measurements extracted from X-ray images. Byway of example, these measurements include, without limitation, proximalangle, head offset, and intramedullary canal width. As reflected inTable I, for the same femur, that was X-ray imaged at zero degrees(i.e., a starting point established by the initial X-ray), six degreesof rotation, and fifteen degrees of rotation exhibited differencesproximal angle, head offset, and intramedullary canal width as measuredusing pixels, where each pixel size was approximately 0.29 millimeters.In particular, proximal angle increased with increasing rotation, as didhead offset, but the same was not true for intramedullary width. In thisexemplary table, three transverse planes were spaced apart along thelongitudinal axis, where each plane corresponded to a location where thewidth of the intramedullary canal was measured. As reflected in Table I,the widths of the intramedullary canal for the same location changedepending upon the angle of rotation. Consequently, as will be discussedin more detail hereafter, when constructing a 3D virtual model of a boneusing X-rays, one must account for rotational deviation to the extentbone rotation occurs during imaging.

It should be understood, however, that the foregoing is an exemplarydescription of anatomies that may be used with the exemplary system andmethods and, therefore, is in no way intended to limit other anatomiesfrom being used with the present system pursuant to the disclosedmethods. As used herein, tissue includes bone, muscle, ligaments,tendons, and any other definite kind of structural material with aspecific function in a multicellular organism. Consequently, when theexemplary system and methods are discussed in the context of bone, thoseskilled in the art should realize the applicability of the system andmethods to other tissue.

Referring back to FIG. 1, the anatomy data input to the system isdirected to three modules, two of which involve processing of theanatomy data (full bone reconstruction module, patient-specific module),while a third (abnormal database module) catalogues the anatomy data aspart of a database. A first of the processing modules, the full bonereconstruction module, processes the input anatomy data with datareceived from the statistical atlas module to generate a virtual, 3Dmodel of the bone(s) in question. This 3D model is a full, normalreconstruction of the bone(s) in question. A second of the processingmodules, the patient-specific module, processes the input anatomy datawith data received from the full bone reconstruction module to generateone or more molds, fixation systems, graft shaping tools, andrenderings, in addition to one or more final orthopedic implants. Arendering refers to visualization of reconstructed anatomy for feedbackregarding expected surgical outcome. More specifically, thepatient-specific module is adapted to generate fully customized devices,designed to precisely fit patient-specific anatomy, despite severedeviation of the patient's anatomy from normal. Moreover, thepatient-specific module utilizes the virtual 3D reconstructed bone modelfrom the full bone reconstruction module to automatically identifyanatomical regions and features for device design parameters (e.g.,fitting region and/or shape). In this fashion, patient-specific data isused to define design parameters so that the output instrument and anyimplant precisely fits the specific anatomy of the patient. Exemplaryutilizations of the patient-specific module will be discussed in greaterdetail hereafter. In order to understand the functions and processes ofthe system in further detail, the following is an explanation of themodules of the system starting with the statistical atlas module.

As shown in FIGS. 1 and 2, the statistical atlas module logs virtual, 3Dmodels of one or more anatomies (e.g., bones) to capture the inherentanatomical variability in a given population. In exemplary form, theatlas logs mathematical representations of anatomical features of theone or more anatomies represented as a mean representation andvariations about the mean representation. By representing the anatomicalfeatures as mathematical representations, the statistical atlas allowsautomated measurements of anatomies and, as will be discussed in moredetail hereafter, reconstruction of missing anatomies.

In order to extract anatomical variations across a common anatomy, inputanatomy data is compared to a common frame of reference across apopulation, commonly referred to as a template 3D model or anatomical 3Dtemplate model. This template 3D model is visually represented on agraphic display as a 3D model that can be rotated and otherwise visuallymanipulated, but comprises a mathematical representation of anatomicalsurface features/representations for all anatomies across thestatistical atlas for the tissue in question (i.e., for a given bone allproperties of the bone are shared across the population of thestatistical atlas, which is generated from the template 3D model). Thetemplate 3D model can be a combination of multiple anatomicalrepresentations or a single representative instance and may representthe lowest entropy state of the statistical atlas. For each anatomy tobe added to the statistical atlas (i.e., input anatomy data), ananatomical 3D model is created and both the anatomical 3D model and thetemplate 3D model are subjected to a normalization process.

During the normalization process, the anatomical 3D model is normalizedrelative to the scale of the template 3D model. The normalizationprocess may involve scaling one or both of the anatomical 3D model andthe template 3D model to have a common unit scale. After normalizationof the anatomical 3D model and the template 3D model, the normalizedanatomical 3D model and template 3D model are rendered scale invariant,so that shape features can be utilized independent of scale (meaningsize in this case). After normalization is complete, both 3D models areprocessed via a scale space mapping and feature extraction sequence.

Scale space mapping and feature extraction is essentially amulti-resolution feature extraction process. In particular, this processextracts shape-specific features at multiple feature scales. Initially,a plurality of anatomical features is selected, each representingfeatures present at a different scale space. Thereafter, for each scalespace representation of the selected anatomical feature, model specificfeatures are extracted. These extracted features are used to draw outrobust (as to noise) registration parameters between the template 3Dmodel and the anatomical 3D model. Subsequent to this multi-resolutionfeature extraction process, the extracted data is processed via amulti-resolution 3D registration process.

Referring to FIGS. 2-5, the multi-resolution 3D registration processuses the scale space extracted features to carry out an affineregistration calculation between the anatomical 3D model and template 3Dmodel in order to register the two models. In particular, the anatomical3D model and template 3D model are processed via a rigid registrationprocess. As represented in FIG. 5, this rigid registration process isoperative to align the anatomical 3D model and template 3D model toensure both models are in the same space and with no pose singularity.In order to align the 3D models, the centroids associated with eachmodel are aligned. In addition, the principle axes for each 3D model arealigned so that the major direction of both 3D models is the same.Finally, the pose difference between the 3D models is minimized bycarrying out an iterative closest point calculation.

Post rigid registration, the 3D models are registered using a similarityregistration process. This process involves aligning the template 3Dmodel and the anatomical 3D model in normal scale iteratively bycalculating a similarity transform that best aligns the normal scalefeatures (i.e., ridges) for both the template 3D model and theanatomical 3D model. The iterative similarity alignment algorithm is avariant of iterative closest point. Within each iteration rotation,translation and scale are calculated between point pairs untilconvergence. Pair matching or correspondence between the two set ofpoints is evaluated using distance query calculated using Kd-tree, orsome other space partitioning data structure. In particular, the ridgesfor both models are utilized to carry out a calculate matching pointpairs process. In this exemplary description, ridges refers to points ona 3D model where a single principle curvature has extrema along itscurvature lines. As part of the calculate matching point pairs process,points are identified on ridges of the 3D models that match one another.Next, the ridges of both 3D models are subjected to a similaritytransformation calculation process where rotation, translation, andscale are calculated that best align the ridges of both models. Atransform points process follows, which is operative to apply thecalculated rotation, translation, and scale to the template 3D modelridges. Thereafter, the root mean square error or distance error betweeneach matched point set is calculated, followed by calculation of thechange in relative root mean square error or distance error from theprevious process. If the change in relative root mean square error ordistance error is within a predetermined threshold, then atransformation process occurs to apply the final rotation, translation,and scale to the template 3D model.

An articulated registration process follows the similarity registrationprocess and receives input data from a scale space features process. Inthe scale space feature process, feature are extracted from the template3D model and the anatomical 3D model in different scale spaces. Eachscale space is defined by convolving the original anatomical 3D modelwith Gaussian smoothing function.

The purpose of the articulated registration process is to match “n”scale space features of the template 3D model with “m” scale spacefeatures calculated on the anatomical 3D model. The difference betweenthe number of detected features on the template 3D model and theanatomical 3D model is due to anatomical variation. This difference in anumber of detected features may result in many relationships between thetemplate 3D model and the anatomical 3D model. Therefore, a two-way,mutual feature matching is performed to accommodate such variation andachieve accurate matching between all mutual features. Specifically,feature sets are computed on the template 3D model in scale space. Inthis exemplary process, feature sets are connected sets of points thatrepresent a prominent anatomical structure (e.g., acetabular cup in thepelvis, spine process in the lumbar). Likewise, feature sets arecomputed on the anatomical 3D model in scale space. A matching featurepair process matches the feature sets computed on the template 3D modelto the feature sets on the anatomical 3D model using shape descriptors(e.g., curvature, shape index, etc.). The result of this process is an“n-m” mapping of feature sets between the template 3D model and theanatomical 3D model. If necessary, a regrouping process is carried outto regroup the matched feature sets into a single feature set (e.g., ifacetabular cup was detected as two pieces, this process would regroupthe two pieces into one single feature set). Thereafter, a calculationprocess is carried out to calculate the correspondence between eachpoint in matched feature sets on the template 3D model and theanatomical 3D model. An affine calculation transformation processfollows in order to calculate the rotation, translation, and shear thattransform each matched feature set on the template 3D model to itscorresponding feature set on the anatomical 3D model. Thereafter, thetemplate 3D model is transformed using the calculated affinetransformation parameters (i.e., rotation, translation, and shear).Finally, a rigid alignment process is carried out to align each matchedfeature set on the template 3D model and the anatomical 3D model.

A non-rigid registration process, occurring after the articulatedregistration process and the normal scale features process, involvesmatching all surface vertices on the template 3D model to vertices onthe anatomical 3D model and calculating initial correspondence. Thiscorrespondence is then used to calculate deformation fields that moveeach vertex on the template 3D model to the matched point on theanatomical 3D model. Matching is done between vertices within the sameclass (i.e., scale space feature vertex; normal scale feature vertex, ornon-feature vertex). In the context of the normal scale featuresprocess, shape features are calculated on the template 3D model and theanatomical 3D model in the original scale space (ridges), meaning theoriginal input model.

Specifically, as part of the non-rigid registration process, the scalespace features are calculated on the template 3D model (TMssf) and onthe anatomical 3D model (NMssf). Each set of features on the template 3Dmodel and on the anatomical 3D model are grown using “k” neighborpoints. An alignment process is applied to the template 3D model scalespace features to match its corresponding feature on the anatomical 3Dmodel. Given two point clouds, reference (X) and moving (Y), the goal isto iteratively align the two point clouds to minimize overall errormetric, under constraint of a minimum relative root mean squared errorand maximum angle threshold. A realignment process is carried out toalign feature sets on the template 3D model with the matching sets onthe anatomical 3D model using iterative closest point in normal scale.Post realignment, the point correspondence between points in eachfeature set on the template 3D model with the matched feature set on theanatomical 3D model is calculated. The matched point on the anatomical3D model should have a surface normal direction close to the template 3Dmodel point. The output is forwarded to the calculate deformation fieldsstep.

Parallel to the scale space features calculation course, template 3Dmodel (TMnfp) and anatomical 3D model (NMnfp) non-feature points or theremaining set of points on the template 3D model surface that does notbelong to either scale space features or normal scale features areprocessed pursuant to a correspondence calculation to calculate thepoint correspondence between non-feature points on the template 3D modeland non-feature points on the anatomical 3D model. The matched point(s)on the new model should have a surface normal direction close to thetemplate model point. The output is forwarded to the calculatedeformation fields step.

Also parallel to the scale space features calculation course, normalscale features (i.e., ridges) on the template 3D model (TM nsf) arealigned with the normal scale features (i.e., ridges) on the anatomical3D model (NM nsf) using AICP. AICP is a variant of the iterative closestpoint calculation where in each iteration translation, rotation, andscale are calculated between matched point sets. After the alignmentprocess, a correspondence process is carried out.

The outputs from scale space features calculation course, thecorrespondence course, and the alignment course are subjected to adeformation process where the deformation field is calculated to moveeach point on the template 3D model to its matched point on theanatomical 3D model.

The output of the non-rigid registration process is a subjected to arelaxation process in order to move the vertices of the template 3Dmodel mesh closer to surface of the anatomical 3D model after themulti-resolution registration step and smooth the output model. Inparticular, the template 3D model in normal space (TM ns) and theanatomical 3D model in normal space (NM ns) are processed via acorrespondence calculation to compute the closest vertices on template3D model to the anatomical 3D model using a normal constrained sphericalsearch algorithm. This calculation, using the closest vertices for bothmodels, generates a correspondence vector from each vertex in thetemplate 3D model and its matched vertices in anatomical 3D model, whichmay result in more than one match point from the anatomical 3D model.Using the matched points for each vertex on the template 3D model, theweighted mean of the matched points on the anatomical 3D model iscalculated based on the Euclidian distance from the point and matchedpoints. At this point, the template 3D model is updated using theweighted average so as to move each point on template 3D model using thecalculated weighted average distance. After the computed weightsprocess, a relaxation process is carried out for every point on templatemodel in order to find the closest point on the anatomical 3D modelsurface and move it to that point. Finally, a smoothing operation isperformed on the deformed template 3D model to remove noise. Theresultant registered 3D models (i.e., template and anatomical 3D models)are then subjected to a free form deformation process.

The free form deformation process morphs the surface of the template 3Dmodel with the surface of the anatomical 3D model. More specifically,the surface of the template 3D model is iteratively moved on a weightedpoint-to-point basis using mutually matched points on both the template3D model surface and the anatomical 3D model surface.

Referencing FIGS. 2 and 6, after the free form deformation process, theanatomical 3D model is subjected to a correspondence calculation processto determine the deviation between the anatomical 3D model and themorphed template 3D model. This correspondence calculation processrefines the template 3D model from the free form deformation step toperform a final match of the selected landmark locations on the templatedeformed 3D model and the deformed anatomical 3D model. In this fashion,the correspondence calculation process calculates and records thevariation in size and shape between the 3D models, which is recorded asdeviation about the mean model. The output of this correspondencecalculation process is the addition of a normalized anatomical 3D modeland a revised template 3D model having been updated to account for thevariations in the anatomical 3D model. In other words, the output of theprocess outlined in FIG. 2 is the normalized anatomical 3D model havingbeen modified to have properties (e.g., point correspondence) consistentwith the revised template 3D model to facilitate full anatomicalreconstruction (e.g., full bone reconstruction).

Referring to FIGS. 1 and 7, inputs from the statistical atlas module andanatomy data are directed to a full anatomy reconstruction module. Byway of example, the anatomy in question may be a bone or multiple bones.It should be noted, however, that anatomies other than bone may bereconstructed using the exemplary hardware, processes, and techniquesdescribed herein. In exemplary form, the full anatomy reconstructionmodule may receive input data as to a partial, deformed, or shatteredpelvis. Input anatomical data comprises two dimensional (2D) images orthree dimensional (3D) surface representations of the anatomy inquestion that may, for example, be in the form of a surface model orpoint cloud. In circumstances where 2D images are utilized, these 2Dimages are utilized to construct a 3D surface representation of theanatomy in question. Those skilled in the art are familiar withutilizing 2D images of anatomy to construct a 3D surface representation.Accordingly, a detailed explanation of this process has been omitted infurtherance of brevity. By way of example, input anatomical data maycomprise one or more of X-rays, computed tomography (CT) scans, magneticresonance images (MRIs), or any other imaging data from which a 3Dsurface representation may be generated. As will be discussed in moredetail hereafter, this input anatomical data may be used, withoutlimitation, for: (1) a starting point for identifying the closeststatistical atlas 3D bone model; (2) registration using a set of 3Dsurface vertices; and, (3) a final relaxation step of reconstructionoutput.

As depicted in FIG. 7, the input anatomical data (e.g., bone model ofthe patient) is utilized to identify the anatomical model (e.g., bonemodel) in the statistical atlas that most closely resembles the anatomyof the patient in question. This step is depicted in FIG. 3 as findingthe closest bone in the atlas. In order to initially identify a bonemodel in the statistical atlas that most closely resembles the patient'sbone model, the patient's bone model is compared to the bone models inthe statistical atlas using one or more similarity metrics. The resultof the initial similarity metric(s) is the selection of a bone modelfrom the statistical atlas that is used as an “initial guess” for asubsequent registration step. The registration step registers thepatient bone model with the selected atlas bone model (i.e., the initialguess bone model) so that the output is a patient bone model that isaligned with the atlas bone model. Subsequent to the registration step,the shape parameters for aligned “initial guess” are optimized so thatthe shape matches the patient bone shape.

Shape parameters, in this case from the statistical atlas, are optimizedso that the region of non-deformed or existing bone is used to minimizethe error between the reconstruction and patient bone model. Changingshape parameter values allows for representation of different anatomicalshapes. This process is repeated, at different scale spaces, untilconvergence of the reconstructed shape is achieved (possibly measured asrelative surface change between iterations or as a maximum number ofallowed iterations).

A relaxation step is performed to morph the optimized tissue to bestmatch the original patient 3D tissue model. Consistent with theexemplary case, the missing anatomy from the reconstructed pelvis modelthat is output from the convergence step is applied to thepatient-specific 3D pelvis model, thereby creating a patient-specific 3Dmodel of the patient's reconstructed pelvis. More specifically, surfacepoints on the reconstructed pelvis model are relaxed (i.e., morphed)directly onto the patient-specific 3D pelvis model to best match thereconstructed shape to the patient-specific shape. The output of thisstep is a fully reconstructed, patient-specific 3D tissue modelrepresenting what should be the normal/complete anatomy of the patient.

Referencing FIG. 1, the abnormal database is utilized as a data inputand training for the defect classification module. In particular, theabnormal database contains data specific to an abnormal anatomicalfeature that includes an anatomical surface representation and relatedclinical and demographic data.

Referencing FIGS. 1 and 8, the fully reconstructed, patient-specific 3Dtissue model representing the normal/complete tissue and inputanatomical data (i.e., 3D surface representation or data from which a 3Dsurface representation may be generated) representingabnormal/incomplete tissue from the abnormal database are input to thedefect classification module. This anatomical data from the abnormaldatabase may be a partial anatomy in the case of tissue degeneration ortissue absence resulting from genetics, or this anatomy may be adeformed anatomy resulting from genetics or environmental conditions(e.g., surgical revisions, diseases, etc.), or this anatomy may be ashattered tissue resulting from one or more anatomy breaks. By way ofexample, input anatomical data may comprise one or more of X-rays,computed tomography (CT) scans, magnetic resonance images (MRIs), or anyother imaging data from which a 3D surface representation may begenerated.

The defect classification module pulls a plurality of abnormal 3Dsurface representations from abnormal database coupled with the normal3D representation of the anatomy in question to create a quantitativedefect classification system. This defect classification system is usedto create “templates” of each defect class or cluster. More generally,the defect classification module classifies the anatomical deficiencyinto classes which consist of closely related deficiencies (referring tothose with similar shape, clinical, appearance, or othercharacteristics) to facilitate the generation of healthcare solutionswhich address these deficiencies. The instant defect classificationmodule uses software and hardware to classify the defects automaticallyas a means to eliminate or reduce discrepancies between pre-operativedata and intra-operative observer visualization. Traditionally,pre-operative radiographs have been taken as a means to qualitativelyanalyze the extent of anatomical reconstruction necessary, but thisresulted in pre-operative planning that was hit-or-miss at best.Currently, intra-operative observers make the final determination of theextent of anatomy deficiency and many times conclude that thepre-operative planning relying on radiographs was defective orincomplete. As a result, the instant defect classification moduleimproves upon current classification systems by reducing interobserverand intraobserver variation related to defect classification andproviding quantitative metrics for classifying new defect instances.

As part of the defect classification module t, the module is may take asinput one or more classification types to be used as an initial state.For example, in the context of a pelvis, the defect classificationmodule may use as input defect features corresponding to the AmericanAcademy of Orthopaedic Surgeons (AAOS) D'Antonio et al. bone defectclassification structure. This structure includes four different classesas follows: (1) Type I, corresponding to segmental bone loss; (2) TypeII, corresponding to cavitary bone loss; (3) Type III, corresponding tocombined segmental and cavitary bone loss; and, (4) Type IV,corresponding to pelvis discontinuity. Alternatively, the defectclassification module may be programmed with the Paprosky bone defectclassification structure. This structure includes three differentclasses as follows: (1) Type I, corresponding to supportive rim with nobone lysis; (2) Type II, corresponding to distorted hemispheres withintact supportive columns and less than two centimeters of superomedialor lateral migration; and, (3) Type III, corresponding to superiormigration greater than two centimeters and sever ischial lysis withKohler's line broken or intact. Moreover, the defect classificationmodule may be programmed with the Modified Paprosky bone defectclassification structure. This structure includes six different classesas follows: (1) Type 1, corresponding to supportive rim with nocomponent migration; (2) Type 2A, corresponding to distorted hemispherebut superior migration less than three centimeters; (3) Type 2B,corresponding to greater hemisphere distortion having less than ⅓ rimcircumference and the dome remaining supportive; (4) Type 2C,corresponding to an intact rim, migration medial to Kohler's line, andthe dome remains supportive; (5) Type 3A, corresponding to superiormigration, greater than three centimeters and severe ischial lysis withintact Kohler's line; and, (6) Type 3B, corresponding to superiormigration, greater than three centimeters and severe ischial lysis withbroken Kohler's line and rim defect greater than half the circumference.Using the output classification types and parameters, the defectclassification module compares the anatomical data to that of thereconstructed data to discern which of the classification types theanatomical data most closely resembles, thereby corresponding to theresulting assigned classification.

As an initial step, the add to statistical atlas step involvesgenerating correspondence between normal atlas 3D bone model and theabnormal 3D bone model. More specifically, the 3D bone models arecompared to discern what bone in the normal 3D model is not present inthe abnormal 3D model. In exemplary form, the missing/abnormal bone isidentified by comparing points on the surface of each 3D bone model andgenerating a list of the discrete points on the surface of the normal 3Dbone model that are not present on the abnormal 3D bone model. Thesystem may also record and list (i.e., identify) those surface points incommon between the two models or summarily note that unless recorded aspoints being absent on the abnormal 3D bone model, all other points arepresent in common in both bone models (i.e., on both the normal andabnormal bone models). Accordingly, the output of this step is theabnormal 3D bone model with statistical atlas correspondence and a listof features (points) from the normal atlas 3D bone model indicating ifthat feature (point) is present or missing in the abnormal 3D bonemodel.

After generating correspondence between the normal atlas 3D bone model(generated from the full bone reconstruction module) and the abnormal 3Dbone model (generated from the input anatomical data), themissing/abnormal regions from the abnormal 3D bone model are localizedon the normal atlas 3D bone model. In other words, the normal atlas 3Dbone model is compared to the abnormal 3D bone model to identify andrecord bone missing from the abnormal 3D bone model that is present inthe normal atlas 3D bone model. Localization may be carried out in amultitude of fashions including, without limitation, curvaturecomparison, surface area comparisons, and point cloud area comparisons.Ultimately, in exemplary form, the missing/abnormal bone is localized asa set of bounding points identifying the geometrical bounds of themissing/abnormal region(s).

Using the bounding points, the defect classification module extractsfeatures from the missing/abnormal region(s) using input clinical data.In exemplary form, the extracted features may include shape information,volumetric information, or any other information used to describe theoverall characteristics of the defective (i.e., missing or abnormal)area. These features may be refined based on existing clinical data,such as on-going defect classification data or patient clinicalinformation not necessarily related to the anatomical feature(demographics, disease history, etc.). The output of this step is amathematical descriptor representative of the defective area(s) that areused in a subsequent step to group similar tissue (e.g., bone)deformities.

The mathematical descriptor is clustered or grouped based upon astatistical analysis. In particular, the descriptor is statisticallyanalyzed and compared to other descriptors from other patients/cadaversto identify unique defect classes within a given population. Obviously,this classification is premised upon multiple descriptors from multiplepatients/cadavers that refine the classifications and identifications ofdiscrete groups as the number of patients/cadavers grows. The outputfrom this statistical analysis is a set of defect classes that are usedto classify new input anatomical data and determines the number oftemplates.

The output of the defect classification module is directed to a templatemodule. In exemplary form, the template module includes data that isspecific as to each of the defect classifications identified by thedefect classification module. By way of example, each template for agiven defect classification includes surface representations of thedefective bone, location(s) of the defect(s), and measurements relatingto the defective bone. This template data may be in the form of surfaceshape data, point cloud representations, one or more curvature profiles,dimensional data, and physical quantity data. Outputs from the templatemodule and the statistical atlas are utilized by a mass customizationmodule to design, test, and allow fabrication of mass customizedimplants, fixation devices, instruments or molds. Exemplary utilizationsof the mass customization module will be discussed in greater detailhereafter.

Patient-Specific Reconstruction Implants

Referring to FIGS. 1 and 17, an exemplary process and system aredescribed for generating patient-specific orthopedic implant guides andassociated patient-specific orthopedic implants for patients afflictedwith partial, deformed, and/or shattered anatomies. For purposes of theexemplary discussion, a total hip arthroplasty procedure will bedescribed for a patient with a partial anatomy. It should be understood,however, that the exemplary process and system are applicable to anyorthopedic implant amenable to patient-specific customization ininstances where incomplete or deformed anatomy is present. For example,the exemplary process and system are applicable to shoulder replacementsand knee replacements where bone degeneration (partial anatomy), bonedeformation, or shattered bones are present. Consequently, though a hipimplant is discussed hereafter, those skilled in the art will understandthe applicability of the system and process to other orthopedicimplants, guides, tools, etc. for use with original orthopedic ororthopedic revision surgeries.

Pelvis discontinuity is a distinct form of bone loss most oftenassociated with total hip arthroplasty (THA), in which osteolysis oracetabular fractures can cause the superior aspect of the pelvis tobecome separated from the inferior portion. The amount and severity ofbone loss and the potential for biological in-growth of the implant aresome of the factors that can affect the choice of treatment for aparticular patient. In the case of severe bone loss and loss of pelvicintegrity, a custom tri-flange cup may be used. First introduced in1992, this implant has several advantages over existing cages. It canprovide stability to pelvic discontinuity, eliminate the need forstructural grafting and intraoperative contouring of cages, and promoteosseointegration of the construct to the surrounding bone.

Regardless of the context, whether partial, deformed, and/or shatteredanatomies of the patient are at issue, the exemplary system and processfor generating patient-specific implants and/or guides utilizes theforegoing exemplary process and system of 3D bone model reconstruction(see FIGS. 1-7 and the foregoing exemplary discussion of the same) togenerate a three dimensional model of the patient's reconstructedanatomy. More specifically, in the context of total hip arthroplastywhere pelvis discontinuity is involved, the exemplary patient-specificsystem utilizes the patient pelvis data to generate a 3D model of thepatient's complete pelvis, which is side specific (right or left).Consequently, a discussion of the system and process for utilizingpatient anatomy data for a partial anatomy and generating a 3Dreconstructed model of the patient's anatomy is omitted in furtheranceof brevity. Accordingly, a description of the process and system forgenerating patient-specific orthopedic implant guides and associatedpatient-specific orthopedic implants for patients afflicted withpartial, deformed, and/or shattered anatomies will be described postformation of the three dimensional reconstructed model.

Referring specifically to FIGS. 17-19 and 23, after the patient-specificreconstructed 3D bone model of the pelvis and femur are generated, boththe incomplete patient-specific 3D bone model (for pelvis and femur) andthe reconstructed 3D bone model (for pelvis and femur) are utilized tocreate the patient-specific orthopedic implant and a patient-specificplacement guide for the implant and/or its fasteners. In particular, theextract defect shape step includes generating correspondence between thepatient-specific 3D model and the reconstructed 3D model (correspondencebetween pelvis models, and correspondence between femur models, but notbetween one femur model and a pelvis model). More specifically, the 3Dmodels are compared to discern what bone in the reconstructed 3D modelis not present in the patient-specific 3D model. In exemplary form, themissing/abnormal bone is identified by comparing points on the surfaceof each 3D model and generating a list of the discrete points on thesurface of the reconstructed 3D model that are not present on thepatient-specific 3D model. The system may also record and list (i.e.,identify) those surface points in common between the two models orsummarily note that unless recorded as points being absent on thepatient-specific 3D model, all other points are present in common inboth models (i.e., on both the reconstructed and patient-specific 3Dmodels).

Referring to FIG. 18, after generating correspondence between thereconstructed 3D model (generated from the full bone reconstructionmodule) and the patient-specific 3D model (generated from the inputanatomical data), the missing/abnormal regions from the patient-specific3D model are localized on the reconstructed 3D model. In other words,the reconstructed 3D model is compared to the patient-specific 3D modelto identify and record bone missing from the patient-specific 3D modelthat is present in the reconstructed 3D model. Localization may becarried out in a multitude of fashions including, without limitation,curvature comparison, surface area comparisons, and point cloud areacomparisons. Ultimately, in exemplary form, the missing/abnormal bone islocalized and the output comprises two lists: (a) a first listidentifying vertices corresponding to bone of the reconstructed 3D modelthat is absent or deformed in the patient-specific 3D model; and, (b) asecond list identifying vertices corresponding to bone of thereconstructed 3D model that is also present and normal in thepatient-specific 3D model.

Referencing FIGS. 18, 19, and 23, following the extract defect shapestep, an implant loci step is performed. The two vertices lists from theextract defect shape step and a 3D model of a normal bone (e.g., pelvis,femur, etc.) from the statistical atlas (see FIGS. 1 and 2, as well asthe foregoing exemplary discussion of the same) are input to discern thefixation locations for a femoral or pelvic implant. More specifically,the fixation locations (i.e., implant loci) are automatically selectedso that each is positioned where a patient has residual bone.Conversely, the fixation locations are not selected in defect areas ofthe patient's residual bone. In this manner, the fixation locations arechosen independent of the ultimate implant design/shape. The selectionof fixation locations may be automated using shape information andstatistical atlas locations.

As show in FIG. 18, after the implant loci step, the next step is togenerate patient-specific implant parameters. In order to complete thisstep, an implant parameterized template is input that defines theimplant by a set number of parameters that are sufficient to define theunderlying shape of the implant. By way of example, in the case of apelvis reconstruction to replace/augment an absent or degenerativeacetabulum, the implant parameterized template includes angle parametersfor the orientation of the replacement acetabular cup and depthparameters to accommodate for dimensions of the femoral head. Otherparameters for an acetabular implant may include, without limitation,the acetabular cup diameter, face orientation, flange locations andshapes, location and orientation of fixation screws. In the case ofporous implants, the location and structural characteristics of theporosity should be included. By way of example, in the case of a femoralreconstruction to replace/augment an absent or degenerative femur, theimplant parameterized template includes angle parameters for theorientation of the replacement femoral head, neck length, head offset,proximal angle, and cross-sectional analysis of the exterior femur andintercondylar channel. Those skilled in the art will understand that theparameters chosen to define the underlying shape of the implant willvary depending upon the anatomy being replaced or supplemented.Consequently, an exhaustive listing of parameters that are sufficient todefine the underlying shape of an implant is impractical. Nevertheless,as depicted in FIG. 19 for example, the reconstructed 3D pelvis modelmay be utilized to obtain the radius of the acetabular cup,identification of pelvic bone comprising the acetabular cupcircumferential upper ridge, and identification of the orientation ofthe acetabular cup with respect to the residual pelvis. Moreover, theparameters may be refined taking into account the implant loci so thatthe implant best/better fits the patient-specific anatomy.

Subsequent to finalizing the set number of parameters that aresufficient to define the underlying shape of the implant, the design ofthe implant is undertaken. More specifically, an initial iteration ofthe overall implant surface model is constructed. This initial iterationof the overall implant surface model is defined by a combination ofpatient-specific contours and estimated contours for the implantedregion. The estimated contours are determined from the reconstructed 3Dbone model, missing anatomical bone, and features extracted from thereconstructed 3D bone model. These features and the location of theimplant site, which can be automatically determined, are used todetermine the overall implant shape, as depicted for example in FIG. 19for an acetabular cup implant.

Referring back to FIG. 17, the initial iteration of the overall implantsurface model is processed pursuant to a custom (i.e., patient-specific)planning sequence. This custom planning sequence may involve inputs froma surgeon and an engineer as part of an iterative review and designprocess. In particular, the surgeon and/or engineer may view the overallimplant surface model and the reconstructed 3D bone model to determineif changes are needed to the overall implant surface model. This reviewmay result in iterations of the overall implant surface model untilagreement is reached between the engineer and surgeon. The output fromthis step is the surface model for the final implant, which may be inthe form of CAD files, CNC machine encoding, or rapid manufacturinginstructions to create the final implant or a tangible model.

Referring to FIGS. 17, 19, and 20, contemporaneous with or after thedesign of the patient-specific orthopedic implant is the design of apatient specific placement guide. In the context of an acetabular cupimplant, as discussed in exemplary form above, one or more surgicalinstruments can be designed and fabricated to assist in placing thepatient-specific acetabular cup. Having designed the patient-specificimplant to have a size and shape to match that of the residual bone, thecontours and shape of the patient-specific implant may be utilized andincorporated as part of the placement guide.

In exemplary form, the acetabular placement guide comprises threeflanges that are configured to contact the ilium, ischium, and pubissurfaces, where the three flanges are interconnected via a ring.Moreover, the flanges of the placement guide may take on the identicalshape, size, and contour of the acetabular cup implant so that theplacement guide will take on the identical position as planned for theacetabular cup implant. In other words, the acetabular placement guideis shaped as the negative imprint of the patient anatomy (ilium,ischium, and pubis partial surfaces), just as the acetabular cup implantis, so that the placement guide fits on the patient anatomy exactly. Butthe implant guide differs from the implant significantly in that itincludes one or more fixation holes configured to guide drilling forholes and/or placement of fasteners. In exemplary form, the placementguide includes holes sized and oriented, based on image analysis (e.g.,microCT), to ensure proper orientation of any drill bit or other guide(e.g., a dowel) that will be utilized when securing the acetabular cupimplant to the residual pelvis. The number of holes and orientationvaries depending upon the residual bone, which impacts the shaped of theacetabular cup implant too. FIG. 20 depicts an example of apatient-specific placement guide for use in a total hip arthroplastyprocedure. In another instance, the guide can be made so that it fitsinto the implant and guides only the direction of the fixation screws.In this form, the guide is shaped as the negative of the implant, sothat it can be placed directly over the implant. Nevertheless, theincorporation of at least part of the patient-specific reconstructedimplant size, shape, and contour is a theme that carries over regardlessof the intended bone to which the patient-specific implant will becoupled.

Utilizing the exemplary system and method described herein can provide awealth of information that can result in higher orthopedic placementaccuracy, better anatomical integration, and the ability topre-operatively measure true angles and plane orientation via thereconstructed three dimensional model.

Creation of Customized Implants Using Massively Customizable Components

Referring to FIG. 22, an exemplary process and system are described forgenerating customized orthopedic implants using massively customizablecomponents. For purposes of the exemplary discussion, a total hiparthroplasty procedure will be described for a patient with severeacetabular defects. It should be understood, however, that the exemplaryprocess and system are applicable to any orthopedic implant amenable tomass customization in instances where incomplete anatomy is present.

Severe acetabular defects require specialized procedures and implantcomponents to repair. One approach is the custom triflange, which afully custom implant consisting of an acetabular cup and three flangesthat are attached to the ilium, ischium, and pubis. In contrast to theexemplary process and system, prior art triflange implants comprise asingle complex component, which is cumbersome to manufacture andrequires that the entire implant be redesigned for every case (i.e.,completely patient-specific). The exemplary process and system generatesa custom triflange implant that makes use of massively customizablecomponents in addition to fully custom components in a modular way toallow custom fitting and porosity.

A preplanning step in accordance with the exemplary process is performedto determine the orientation of the three flanges relative to the cup,the flange contact locations, and the acetabular cup orientation andsize. This preplanning step is conducted in accordance with the“Patient-specific Implants” discussion immediately preceding thissection. By way of example, specific locations of implant fixation aredetermined pursuant to an implant loci step and using its prefatory datainputs as discussed in the immediately preceding section. By way ofrecall, as part of this implant loci step, the two vertices lists fromthe extract defect shape step and a 3D model of a normal pelvis from thestatistical atlas (see FIGS. 1 and 2, as well as the foregoing exemplarydiscussion of the same) are input to discern the fixation locations forthe custom triflange. More specifically, the fixation locations (i.e.,implant loci) are selected so that each is positioned where a patienthas residual bone. In other words, the fixation locations are notselected in defect areas of the patient's residual pelvis. In thismanner, the fixation locations are chosen independent of the ultimateimplant design/shape.

After determining the fixation locations, the triflange components(i.e., flanges) are designed using the “Patient-specific Implants”discussion immediately preceding this section. The flanges are designedto be oriented relative to the replacement acetabular cup so that thecup orientation provides acceptable joint functionality. Additionally,the contact surfaces of the flanges are contoured to match the patient'spelvis anatomy in that the contact surfaces of the triflanges are shapedas a “negative” of the pelvis's bony surface. The exemplary process ofFIG. 20 utilizes the final step of the process depicted in FIG. 14 torapid prototype the flanges (or use conventional computer numericalcontrol (CNC) equipment). After the flanges are fabricated, furthermachining or steps may be performed to provide cavities within whichporous material may be added to the triflanges.

One portion of the triflange system that does not need to be a customcomponent is the acetabular cup component. In this exemplary process, afamily of acetabular cups is initially manufactured and provides thefoundation on which to build the triflange system. These “blank” cupsare retained in inventory for use as needed. If a particular porosityfor the cup is desired, mechanical features are added to the cup thatallows press fitting of porous material into the cup. Alternatively, ifa particular porosity for the cup is desired, the cup may be coatedusing one or more porous coatings.

After the blank cup is formed and any porosity issues are addressed asdiscussed above, the cup is rendered patient-specific by machining thecup to accept the flanges. In particular, using the virtual model of theflanges, the system software constructs virtual locking mechanisms forthe flanges, which are transformed into machine coding so that thelocking mechanisms are machined into the cup. These locking mechanismsallow the cup to be fastened to the flanges so that when the flanges aremounted to the patient's residual bone, the cup is properly orientedwith respect to the residual pelvis. This machining may use conventionalCNC) equipment to form the locking mechanisms into the blank cups.

Subsequent to fabrication of the locking mechanisms as part of the blankcup, the flanges are mounted to the cup using the interface between thelocking mechanisms. The triflange assembly (i.e., final implant) issubjected to an annealing process to promote strong bonding between thecomponents. Post annealing of the triflange implant, a sterilizationprocess occurs followed by appropriate packaging to ensure a sterileenvironment for the triflange implant.

Creation of Mass Customized Implants

Referring to FIG. 24, an exemplary process and system are described forgenerating mass customized orthopedic implant guides and associated masscustomized orthopedic implants for patients afflicted with partial,deformed, and/or shattered anatomies. For purposes of the exemplarydiscussion, a total hip arthroplasty procedure will be described for apatient needing primary joint replacement. It should be understood,however, that the exemplary process and system are applicable to anyorthopedic implant and guides amenable to mass customization ininstances where incomplete anatomy is present. For example, theexemplary process and system are applicable to shoulder replacements andknee replacements where bone degeneration (partial anatomy), bonedeformation, or shattered bones are present. Consequently, though a hipimplant is discussed hereafter, those skilled in the art will understandthe applicability of the system and process to other orthopedicimplants, guides, tools, etc. for use with primary orthopedic ororthopedic revision surgeries.

The exemplary process utilizes input data from a macro perspective and amicro perspective. In particular, the macro perspective involvesdetermination of the overall geometric shape of the orthopedic implantand corresponding anatomy. Conversely, the micro perspective involvesaccounting for the shape and structure of cancellous bone and itsporosity.

The macro perspective includes a database communicating with astatistical atlas module that logs virtual, 3D models of one or moreanatomies (e.g., bones) to capture the inherent anatomical variabilityin a given population. In exemplary form, the atlas logs mathematicalrepresentations of anatomical features of the one or more anatomiesrepresented as a mean representation and variations about the meanrepresentation for a given anatomical population. Reference is had toFIG. 2 and the foregoing discussion of the statistical atlas and how oneadds anatomy to the statistical atlas of a given population. Outputsfrom the statistical atlas are directed to an automatic landmarkingmodule and to a surface/shape analysis module.

The automatic landmarking module utilizes inputs from the statisticalatlas (e.g., regions likely to contain a specific landmark) and localgeometrical analyses to calculate anatomical landmarks for each instanceof anatomy within the statistical atlas. This calculation is specific toeach landmark. The approximate shape of the region is known, forexample, and the location of the landmark being searched for is knownrelative to the local shape characteristics. For example, locating themedial epicondylar point of the distal femur is accomplished by refiningthe search based on the approximate location of medial epicondylarpoints within the statistical atlas. Accordingly, it is known that themedial epicondylar point is the most medial point within this searchwindow, so a search for the most medial point is performed as to eachbone model within the medial epicondylar region defined in thestatistical atlas, with the output of the search being identified as themedial epicondylar point landmark. After the anatomical landmarks areautomatically calculated for each virtual, 3D model within thestatistical atlas population, the virtual, 3D models of the statisticalatlas are directed to a feature extraction module, along withshape/surface analysis outputs.

The shape/surface outputs come from a shape/surface module alsoreceiving inputs from the statistical atlas. In the context of theshape/surface module, the virtual, 3D models within the statisticalatlas population are analyzed for shape/surface features that are notencompassed by the automatic landmarking. In other words, featurescorresponding to the overall 3D shape of the anatomy, but not belongingto features defined in the previous automatic landmarking step arecalculated as well. For example, curvature data is calculated for thevirtual 3D models.

Outputs from the surface/shape analysis module and the automaticlandmarking module are directed to a feature extraction module. Using acombination of landmarks and shape features, mathematical descriptors(i.e. curvature, dimensions) relevant to implant design are calculatedfor each instance in the atlas. These descriptors are used as input to aclustering process.

The mathematical descriptor is clustered or grouped based upon astatistical analysis. In particular, the descriptor is statisticallyanalyzed and compared to other descriptors from the remaining anatomypopulation to identify groups (of anatomies) having similar featureswithin the population. Obviously, this clustering is premised uponmultiple descriptors from multiple anatomies across the population. Asnew instances are presented to the clustering, which were not present inthe initial clustering, the output clusters are refined to betterrepresent the new population. The output from this statistical analysisis a finite number of implants (including implant families and sizes)covering all or the vast majority of the anatomical population.

For each cluster, a parameterization module extracts the mathematicaldescriptors within the cluster. The mathematical descriptors form theparameters (e.g., CAD design parameters) for the eventual implant model.The extracted mathematical descriptors are fed into an implant surfacegeneration module. This module is responsible for converting themathematical descriptors into surface descriptors to generate a 3D,virtual model of the anatomy for each cluster. The 3D, virtual modelcomplements the micro perspective prior to stress testing and implantmanufacturing.

On the micro perspective, for each anatomy of a given population, datais obtained indicative of structural integrity. In exemplary form, thisdata for a bone may comprise microCT data providing structuralinformation as to the cancellous bone. More specifically, the microCTdata may comprise images of the bone in question (multiple microCTimages for multiple bones across a population). These images arethereafter segmented via the extract trabecular bone structure module inorder to extract the three dimensional geometry of the cancellous bonesand create virtual, 3D models for each bone within the population. Theresulting 3D virtual models are input to a pore size and shape module.As depicted graphically in FIG. 76, the 3D virtual models include poroussize and shape information, which is evaluated by the pore size andshape module to determine pore size and size for the cancellous bone.This evaluation is useful to analyze the porous size and shape of thebone within the intramedullary canal so that the stem of the femoralimplant can be treated with a coating or otherwise processed to exhibita porous exterior to promote integration between the residual bone ofthe femur and the femoral implant. The output from this module, incombination with the 3D virtual model output from the implant surfacegeneration module, is directed to a virtual stress testing module.

The stress testing module combines implant porosity data from the poresize and shape module and implant shape data from the implant surfacegeneration module to define the final implant shape model andproperties. For example, the shape and properties include providing aporous coating for the final implant model that roughly matches thecancellous bone porosity for the bone in question. Once the shape andproperties are incorporated, the final implant model undergoes virtualstress testing (finite-element and mechanical analysis) to verify thefunctional quality of the model. To the extent the functional quality isunacceptable, the parameters defining the implant shape and porosity aremodified until acceptable performance is achieved. Presuming the finalimplant model satisfies the stress testing criteria, the final implantmodel is utilized to generate machine instructions necessary to convertthe virtual model into a tangible implant (that may be further refinedby manufacturing processes known to those skilled in the art). Inexemplary form, the machine instructions may include rapid manufacturingmachine instructions to fabricate the final implant through a rapidprototyping process (to properly capture porous structure) or acombination of traditional manufacturing and rapid prototyping.

Creation of Gender/Ethnic Specific Hip Implants

Referring to FIGS. 25-76, an exemplary process and system are describedfor generating gender and/or ethnic specific implants. For purposes ofthe exemplary discussion, a total hip arthroplasty procedure will bedescribed for a patient with requiring primary joint replacement. Itshould be understood, however, that the exemplary process and system areapplicable to any orthopedic implant amenable to customization. Forexample, the exemplary process and system are applicable to shoulderreplacements and knee replacements and other primary joint replacementprocedures. Consequently, though a hip implant is discussed hereafter,those skilled in the art will understand the applicability of the systemand process to other orthopedic implants, guides, tools, etc. for usewith original orthopedic or orthopedic revision surgeries.

The hip joint is composed of the head of the femur and the acetabulum ofthe pelvis. The hip joint anatomy makes it one of the most stable jointsin the body. The stability is provided by a rigid ball and socketconfiguration. The femoral head is almost spherical in its articularportion that forms two-thirds of a sphere. Data has shown that thediameter of the femoral head is smaller for females than males. In thenormal hip, the center of the femoral head is assumed to coincideexactly with the center of the acetabulum and this assumption is used asthe basis for the design of most hip systems. However, the nativeacetabulum is not deep enough to cover all of the native femoral head.The almost rounded part of the femoral head is spheroidal rather thanspherical because the uppermost part is flattened slightly. Thisspheroidal shape causes the load to be distributed in a ring-likepattern around the superior pole.

The geometrical center of the femoral head is traversed by three axes ofthe joint: the horizontal axis; the vertical axis; and, theanterior/posterior axis. The femoral head is supported by the neck ofthe femur, which joints the shaft. The axis of the femoral neck isobliquely set and runs superiorly medially and anteriorly. The angle ofthe inclination of the femoral neck to the shaft in the frontal plane isthe neck shaft angle. In most adults, this angle varies between 90 to135 degrees and is important because it determines the effectiveness ofthe hip abductors, the length of the limb, and the forces imposed on thehip joint.

An angle of inclination greater than 125 degrees is called coxa valga,whereas an angle of inclination less than 125 degrees is called coxavara. Angles of inclination greater than 125 degrees coincide withlengthened limbs, reduced effectiveness of the hip abductors, increasedload on the femoral head, and increased stress on the femoral neck. In acase of coxa vara, angles of inclination less than 125 degrees coincidewith shortened the limbs, increased effectiveness of the hip abductors,decreased load on the femoral head, and decreased stress on the femoralneck. The femoral neck forms an acute angle with the transverse axis ofthe femoral condyles. This angle faces medially and anteriorly and iscalled angle of anteversion. In adult humans, this angle averagesapproximately 7.5 degrees.

The acetabulum lies on the lateral aspect of the hip where the ilium,ischium, and pubis meet. These three separate bones join into theformation of the acetabulum, with the ilium and ischium contributingapproximately two-fifths each and the pubis one-fifth of the acetabulum.The acetabulum is not a deep enough socket to cover all of the femoralhead and has both articulating and non-articulating portions. However,the acetabular labrum deepens the socket to increase stability. Togetherwith labrum, the acetabulum covers slightly more than 50% of the femoralhead. Only the sides of the acetabulum are lined with articularcartilage, which is interrupted inferiorly by the deep acetabular notch.The center part of the acetabular cavity is deeper than the articularcartilage and is nonarticular. This center part is called the acetabularfossae and is separated from the interface of the pelvic bone by a thinplate. The acetabular fossae is a region unique for every patient and isused in creating patient-specific guide for reaming and placement of theacetabular cup component. Additionally, variation of anatomical featuresfurther warrant the need for population specific implant designs.

Some of the problems associated with prior art use of cementlesscomponents can be attributed to the wide variation in size, shape, andorientation of the femoral canal. One of the challenges to orthopedicimplant design of the femoral stem is large variation in themediolateral and anteroposterior dimensions. There is also significantvariation in the ratio of the proximal to distal canal size. Thedifferent combination of various arcs, taper angles, curves, and offsetsin the normal population is staggering. However, that is not the onlyproblem.

Ancestral differences in femora morphology and a lack of definitestandards for modern populations makes designing the proper hip implantsystem problematic. For example, significant differences in anteriorcurvature, torsion, and cross-sectional shape exist between AmericanIndians, American blacks, and American whites. Differences between Asianand Western populations in the femora are found in the anterior bow ofthe femora, where Chinese are more anteriorly bowed and externallyrotated with smaller intramedullary canals and smaller distal condylesthan Caucasian femora. Likewise, Caucasian femora are larger thanJapanese femora in terms of length distal condyle dimensions. Ethnicdifferences also exist in the proximal femur mineral bone density (BMD)and hip axis length between American blacks and whites. The combinedeffects of higher BMD, shorter hip axis length, and shorterintertrochanteric width may explain the lower prevalence of osteoporoticfractures in American black women compared to their white counterparts.Similarly, elderly Asian and American black men were found to havethicker cortices and higher BMD than white and Hispanic men, which maycontribute to greater bone strength in these ethnic groups. In general,American blacks have thicker bone cortices, narrower endostealdiameters, and greater BMD than American whites.

Combining the femur and the pelvic ancestral (and ethnic) differencesbecomes even more challenging to primary hip systems. Revision surgerycreates more complexity. Added to these normal anatomic and ethnicvariations, the difficulties faced by the surgeon who performs revisionoperation are compounded by: (a) distortion of the femoral canal causedby bone loss around the originally placed prostheses; and, (b)iatrogenic defects produced by the removal of the components and cement.

All of the foregoing factors have led a number of hip surgeons to lookfor ways to improve design of uncemented femoral prostheses. In totalhip replacement (primary or revision), the ideal is to establish anoptimal fit between the femoral ball and acetabular cup. The femoralstem neck should have a cruciform cross section to reduce stiffness. Thestem length should be such that the stem has parallel contact with thewalls of the femur over two to three internal canal diameters. Theproximal one third of the stem is porous coated or hydroxylapatite (HA)coated. The stem is cylindrical (i.e. not tapered) to control bendingloads and to allow transmission of all rotational and axial loadsproximally. The femoral head position should reproduce the patient's ownhead center, unless it is abnormal.

One way to attempt to satisfy these goals is to manufacture femoralprostheses individually for each patient. In other words, make aprosthesis that is specific to a particular patient rather than tryingto reshape the patient's bone to fit a readymade prosthesis.

There are some common design rules for patient-specific (or masscustomization) primary and revision hip replacements. Among these designrules are: (1) the hip stem should be collarless (except in revision) toallow uniform distribution of load to the femur; (2) the hip stem shouldhave a modified rhomboidal cross section to maximize fit/fill, butshould maintain rotational stability; (3) the hip stem should be bowedwhen necessary to conform to patient's bone; (4) the hip stem should beinserted along a curved path, with no gaps between the prosthesis andthe bone; (5) the hip stem neck should have cruciform cross section toreduce stiffness; (6) the hip stem length should be such that the stemhas parallel contact with the walls of the femur over two to threeinternal canal diameters; (7) the proximal one third of the hip stem isporous coated or hydroxylapatite (HA) coated; (8) the hip stem iscylindrical (i.e. not tapered) to control bending loads and to allowtransmission of all rotational and axial loads proximally; (9) thefemoral head position of the hip stem should reproduce the patient's ownhead center, unless it is abnormal.

The following is an exemplary process and system for generating masscustomized orthopedic implant for patients needing primary jointreplacement taking into account the gender and/or ethnicity of thepatient population. For purposes of the exemplary discussion, a totalhip arthroplasty procedure will be described for a patient with apartial anatomy. It should be understood, however, that the exemplaryprocess and system are applicable to any orthopedic implant amenable tomass customization in instances where incomplete anatomy is present. Forexample, the exemplary process and system are applicable to shoulderreplacements and knee replacements where bone degeneration (partialanatomy), bone deformation, or shattered bones are present.Consequently, though a femoral component of a hip implant is discussedhereafter, those skilled in the art will understand the applicability ofthe system and process to other orthopedic implants, guides, tools, etc.for use with original orthopedic or orthopedic revision surgeries.

Referring to FIG. 25, an overall process flow is depicted for using astatistical atlas for generation of both mass customized andpatient-specific hip implants. Initially, the process includes thestatistical atlas including several instances of one or more bones beinganalyzed. In the exemplary context of a hip implant, the statisticalatlas includes several instances of bone models for the pelvis bone andthe femur bone. An articulating surface geometry analysis is conductedat least for the acetabular component (i.e., acetabulum) and theproximal femoral component (i.e., femoral head). In particular, thearticulating surface geometry analysis involves calculation oflandmarks, measurements, and shape features on each bone from a givenpopulation of the statistical atlas. In addition, the articulatingsurface geometry analysis includes generating quantitative values, suchas statistics, representative of the calculations. From thesecalculations, a distribution of the calculations is plotted and parsedbased the distribution. For a bell-shaped distribution, for example, itmay be observed that approximately ninety percent (90%) of thepopulation is grouped so that a non-patient-specific implant (e.g., amass customized implant) may be designed and adequately fit thisgrouping, thereby reducing the costs for patients compared withpatient-specific implants. For the remaining ten percent (10%) of thepopulation, a patient-specific implant may be a better approach.

In the context of a mass customized implant, the statistical atlas maybe utilized to quantitatively assess how many different groups (i.e.,different implants) are able to encompass the overwhelming majority of agiven population. These quantitative assessments may result in clustersof data indicating the general parameters for a basic implant designthat, while not patient-specific, would be more specific than anoff-the-shelf alternative.

In the context of a patient-specific implant, the statistical atlas maybe utilized to quantitatively assess what a normal bone embodies anddifferences between the patient's bone and a normal bone. Morespecifically, the statistical atlas may include curvature data that isassociated with a mean or template bone model. This template bone modelcan then be used to extrapolate what the form of the patient's correctbone would be and craft the implant and surgical instruments used tocarry out the implant procedure.

FIG. 26 graphically summarizes the utilization of a statistical atlas indesigning mass customized and patient-specific hip implants. In thecontext of the implant box, reference is had back to FIGS. 17 and 18 andthe associated discussion for these figures. Similarly, in the contextof the planner box, reference is had back to FIG. 17 and the associateddiscussion of the custom planning interface. Finally, in the context ofthe patient-specific guides box, reference is had back to FIG. 19 andthe associated discussion for this figure.

As depicted in FIG. 27, a flow chart is depicted for an exemplaryprocess that may be utilized to design and fabricate gender and/orethnic specific hip implants. In particular, the process includesutilization of a statistical atlas containing various specimens of aproximal femur (i.e., femur including femoral head) that have beenidentified by associated data as being from either a male or a femaleand the ethnicity of the person from which the bone pertains. Moreover,the statistical atlas module logs virtual, 3D models of one or moreanatomies (e.g., bones) to capture the inherent anatomical variabilityin a given gender and/or ethnic population. In exemplary form, the atlaslogs mathematical representations of anatomical features of the one ormore anatomies represented as a mean representation and variations aboutthe mean representation for a given anatomical population that may havea common gender and/or ethnicity (or grouped to have one of a pluralityof ethnicities for which anatomical commonalities exist). Reference ishad to FIG. 2 and the foregoing discussion of the statistical atlas andhow one adds anatomy to the statistical atlas for a given population.Outputs from the statistical atlas are directed to an automaticlandmarking module and to a surface/shape analysis module.

Referring to FIGS. 27-39, the automatic landmarking module utilizesinputs from the statistical atlas (e.g., regions likely to contain aspecific landmark) and local geometrical analyses to calculateanatomical landmarks for each instance of anatomy within the statisticalatlas. By way of example, various proximal femur landmarks arecalculated for each 3D virtual model of a femur that include, withoutlimitation: (1) femoral head center, which is the center point of afemoral head approximated by a sphere; (2) greater trochanter point,which is the point on the greater trochanter having the minimum distanceto the plane passing through the neck shaft point perpendicular to theanatomical neck center line; (3) osteotomy point, which is the pointfifteen millimeters from the end of the lesser trochanter (approximatelythirty millimeters from the lesser trochanter point); (4) neck shaftpoint, which is the point on the head sphere whose tangential planeencloses the minimum femoral neck cross-sectional area; (5) femur waist,which is the cross-section with the smallest diameter along the femurshaft; (6) intramedullary canal waist, which is the cross-section withthe smallest diameter along the intramedullary canal; (7) femoral neckpivot point, which is the point on the femoral anatomical axis thatforms with the femoral head center and the distal end of the femoralanatomical axis an angle equal to the femoral neck angle; and, (8)lesser trochanter point, which is the point on the lesser trochanterregion that most protrudes outward. By way of further example, variousproximal femur axes are calculated for each 3D virtual model of a femurusing the identified anatomical landmarks that include, withoutlimitation: (a) femoral neck anatomical axis, which is coaxial with aline connecting the femur head center with the femur neck center; (b)femoral neck axis, which is coaxial with a line joining the femur headcenter point and the femoral neck pivot point; and, (c) femoralanatomical axis, which is coaxial with a line connecting two pointslying at a distance twenty-three percent and forty percent of the totalfemur length starting from the proximal end of the femur. By way of yetfurther example, various proximal femur measurements are calculated foreach 3D virtual model of a femur using the identified anatomicallandmarks and axes that include, without limitation: (i) proximal angle,which is the 3D angle between femoral anatomical axis and femoral neckanatomical axis; (ii) head offset, which is the horizontal distancebetween the femoral anatomical axis and the femoral head center; (iii)head height, which is the vertical distance between the lessertrochanter point (referenced previously) and femoral head center; (iv)greater trochantor to head center distance, which is the distancebetween the head center and the greater trochanter point (referencedpreviously); (v) neck length, which is the distance between the headcenter and the neck-pivot point (referenced previously); (vi) the headradius, which is the radius of the sphere fitted to femoral head; (vii)neck diameter, which is the diameter of the circle fitted to the neckcross section at plane normal to femoral neck anatomical axis andpassing through neck center point (referenced previously); (viii)femoral neck anteversion transepicondylar angle, which is the anglebetween the transepicondylar axis and femoral neck axis; (ix) femoralneck anteversion posteriorcondylar angle, which is the angle between theposteriorcondylar axis and femoral neck axis; (x) LPFA, which is theangle between mechanical axis and vector pointing to the greatertrochanter; (xi) calcar index area, which is defined by the equation:(Z−X)/Z, where Z is the femur area at 10 centimeters below the midlesser trochanter point and X is the intramedullary canal area at 10centimeters below the mid lesser trochanter point; (xii) canal calcarratio area, which is the ratio between the intramedullary canal area at3 centimeters below the mid-lesser trochanter level to theintramedullary canal area at 10 centimeters below the mid-lessertrochanter; (xiii) XYR area, which is the ratio between theintramedullary canal area at 3 centimeters below the mid-lessertrochanter to the intramedullary canal area at 10 centimeters below themid-lesser trochanter; (xiv) minor/major axes ratio, which is the ratiobetween the minor axis and major axis of a fitted ellipse to theintramedullary canal cross-section at the narrowest point onintramedullary canal; and, (xv) femur radii to intramedullary canalradii ratio, which is the ratio of circle radii, using circles best fitto the circumference of the outer circumference of the femur andintramedullary canal within a plane normal to the femoral anatomicalaxis (this ratio reflects the thickness of the cortical bone and,accordingly, cortical bone loss in cases of osteoporosis).

Referencing FIGS. 27 and 40-42, using the output from the automaticlandmarking module, parameters for the femoral stem are assessed for agiven population. In particular, regardless of whether the population isgrouped based upon ethnicity, gender, or a combination of the two, themedial contour, neck angle, and head offset are assessed.

In the case of the medial contour, this contour with respect to theintramedullary canal for each femur within the population is generatedby intersecting the intramedullary canal with a plane extending throughthe femoral pivot point and having a normal axis perpendicular to boththe femoral anatomical axis and the neck axis (vectors cross product).After the contours are generated for each femur within the population,the population is subdivided into groups using intramedullary canalsize. When subdivided, the contours may be out of plane, so an alignmentprocess is carried out to align all the contours with respect to acommon plane (e.g., an X-Z plane). The alignment process includesaligning the axis which is normal to both the femoral neck axis andanatomical axis to the Y axis then aligning the anatomical axis to the Zaxis. In this fashion, all contours are translated relative to aspecific point in order for the contours to have a common coordinateframe.

After the contours have a common coordinate frame, the femoral neckpoint is utilized to verify that the points of the contours are inplane. In particular, the femoral neck point is a consistent point thatreflects real anatomy and guarantees the points on the contours are inplane. By verifying the points of the contour are in plane, alignmentvariability between population femurs can be significantly reduced,which facilitates utilization of the contours for head offset andimplant angle design.

Referring to FIG. 43, the statistical atlas may also be useful tointerpolate between normal and osteoporotic bones. When designing andsizing a femoral stem, one of the key considerations is intramedullarycanal dimensions. In instances of normal bone, with respect to thefemur, the intramedullary canal is significantly narrower than comparedto a femur exhibiting osteoporosis. This narrower intramedullary canaldimension is the result, at least in part, of bone thicknesses (measuredtransverse to the dominant axis of the femur) decreasing, whichcorrespondingly results in receding of the interior surface of the femurdelineating the intramedullary channel. In this method, a syntheticpopulation is created by interpolating between healthy and severelyosteoporotic bone thicknesses and generating virtual 3D models havingsaid thicknesses. This dataset thusly contains bones corresponding todifferent stages of osteoporosis. This dataset can now be used as aninput to implant stem design.

In exemplary form, the statistical atlas includes a population ofnormal, non-osteoporotic bones and osteoporotic bones, in this case thebone is a femur. Each of these normal femurs of the atlas is quantifiedand represented as a 3D virtual model, in accordance with the processdescribed herein for adding bones to a statistical atlas. Likewise, eachof the osteoporotic bones of the atlas is quantified and represented asa 3D virtual model, in accordance with the process described herein foradding bones to a statistical atlas. As part of the 3D models for normaland osteoporotic bones, intramedullary canal dimensions are recordedalong the longitudinal length of the femur. Using atlas pointcorrespondence, the intramedullary canal is identified on the atlasbones as spanning a fixed percentage of the overall bone length (say 5%)proximal to the lesser trochanter and a second fixed percentage (say 2%)proximal to the distal cortex point. Additionally, points on theexternal bone surface falling within these proximal and distal boundsare used for determining bone thickness, defined as the distance fromthe external point to the nearest point on the IM canal.

In the context of a proximal femur, FIGS. 46-57 confirm that genderdifferences exist across any ethnic population. As depicted in FIGS. 54and 55, the template 3D model of the statistical atlas for a proximalfemur of a woman exhibits statistical significant measurements whencompared to the template 3D model of a proximal femur for a male. Inparticular, the head offset is approximately 9.3% less for females thanfor males. In current implants head offset increases with stem size,which is acceptable in normal female cases. But a problem arises whenaccounting for head offset in cases of osteoporosis and osteopinia wherethe bone loss leads to increase of intramedullary canal size, whichmeans larger stem size and larger offset. Similarly, the neck diameterand head radius are approximately 11.2% less for females than for males.And the neck length is approximately 9.5% less for females than formales. In addition, the proximal angle is approximately 0.2% less forfemales than for males. Finally, the femoral head height isapproximately 13.3% less for females than for males. Consequently, thegender bone data confirms that simply scaling a generic, femoral implant(i.e., gender neutral) will not account for differences in bonegeometries and, hence, a gender based femoral implant is needed.

Referring to FIGS. 58-63, not only do the dimensions of the proximalfemur widely vary across gender lines, but so too does thecross-sectional shape of the femur along the length of theintramedullary canal. In particular, across a given population within astatistical atlas of male and female femurs, males have intramedullarycanal cross-sections that are closer to circular than females. Morespecifically, females have intramedullary canal cross-sections that are8.98% more eccentric than for males. As will be discussed in more detailhereafter, this gender specific data comprises part of the featureextraction data that is plotted to arrive at clusters from which thenumber and general shape parameters are extracted to arrive at thegender specific femoral implants.

As depicted in FIGS. 64-66, the statistical atlas includes calculationsthat correspond to measurements across a given population of femurs(divided by gender) as to the head center offset in theanterior-posterior (AP) direction. In exemplary form, AP direction wasdetermined by a vector that points anteriorly perpendicular to both themechanical axis and the posterior condylar axis. Offset was measuredbetween the femoral head center and two reference points, with the firstreference point being the midpoint of the anatomical axis, and thesecond reference point being the femur neck pivot point. In summary, APhead height relative to the neck pivot point and anatomical axismidpoint did not exhibit significant differences between male and femalefemurs. Again, this gender specific data comprises part of the featureextraction data that is plotted to arrive at clusters from which thenumber and general shape parameters are extracted to arrive at thegender specific femoral implants.

Referring back to FIGS. 24 and 27, the head center offset,cross-sectional shape data of the intramedullary canal, and medialcontour data for the femurs within the statistical atlas populationcomprise part of the extracted feature data that is plotted to discernthe number of clusters present across a given population (one that isgender specific, a second that is ethnic specific presuming thestatistical atlas includes data as to the ethnicity associated with eachbone) in order to design a gender and/or ethnic specific, masscustomized implant consistent with the flow chart and associateddiscussion for FIG. 24. The identified clusters that are gender and/orethnic specific are utilized to extract the parameters necessary todesign a mass customized femoral implant.

Referring to FIG. 68, an exemplary mass-customized femoral component inaccordance with the instant disclosure is depicted. In particular, themass-customized femoral component comprises four primary elements thatinclude a ball, neck, proximal stem, and distal stem. Each of theprimary elements includes an interchangeable interface to allowinterchangeable balls, necks, and stems with the other interchangeableelements. In this fashion, if a larger femoral ball is needed, only thefemoral ball would be exchanged. Likewise, if a greater neck offset wasdesired, the neck element would be exchanged for a different neckelement providing the requisite offset, while retaining the other threeelements if appropriate. In this manner, the femoral component can,within certain limits, be customized to fit the patient withoutnecessarily sacrificing the fit or kinematics that would otherwise besurrendered by using a one-size-fits-all implant. Accordingly, all ofthe femoral elements can be exchanged for other mass customized elementsto better suit the patient anatomy.

In this exemplary embodiment, the neck is configured to rotate about theaxis of the proximal stem so that the rotational orientation of the neckwith respect to the proximal stem may be adjusted intraoperativly. Inparticular, preoperative measurements may establish the plannedrotational position of the neck with respect to the proximal stem.Nevertheless, intraoperative considerations such as in-vivo kinematictesting may result in the surgeon changing the pre-operative rotationalorientation to provide improved kinematics or avoidance of a particularimpingement. By way of example, the neck includes a cylindrical studhaving an inset circumferential groove having a textured surface. Thiscylindrical stud is received within an axial cylindrical channel of theproximal stem. In addition to this cylindrical channel, a second channelintersects the cylindrical channel and is shaped to receive a platehaving a semi-circular groove that is also textured and configured toengage the textured surface of the inset circumferential groove. A pairof screws fastened to the proximal stem pushes the plate into engagementwith the cylindrical stud so that eventually, rotational motion of thecylindrical stud with respect to the proximal stem is no longerpossible. Accordingly, when this fixed engagement is reached, the screwsmay be loosened to allow rotational motion between the cylindrical studand the proximal stem, such as would be necessary to make rotationaladjustments intraoperatively.

Engagement between the neck and ball may be conventional, whereasengagement between the proximal stem and the distal stem isunconventional. In particular, the proximal stem includes a distal shankthat is threaded and engaged to be threadably received within a threadedopening extending into the distal stem. Accordingly, the proximal stemis mounted to the distal stem by rotation of the proximal stem withrespect to the distal stem so that the threads of the shank engage thethreads of the distal stem opening. Rotation of the proximal stem withrespect to the distal stem is concluded when the proximal stem abuts thedistal stem. However, if rotational adjustment is necessary between theproximal stem and the distal stem, washers may be utilized to provide aspacer corresponding to the correct rotational adjustment. By way offurther example, if greater rotational adjustment is required, thewasher will be greater in thickness, whereas a thinner washer willprovide correspondingly less rotational adjustment.

Each of the primary elements may be fabricated in predeterminedalternatives that account for size and contour variations within a givengender and/or ethnicity. In this fashion, the alternatives of theprimary elements may be mixed and matched to approximate apatient-specific implant that more closely configures to the anatomy ofthe patient than conventional mass customized femoral components, but ata fraction of the cost and process utilized to generate apatient-specific femoral implant.

FIG. 69 depicts a further alternate exemplary mass-customized femoralcomponent in accordance with the instant disclosure is depicted. Inparticular, the mass-customized femoral component comprises five primaryelements that include a ball, neck, proximal stem, intermediate stem,and distal stem. Each of the primary elements includes aninterchangeable interface to allow interchangeable balls, necks, andstems with the other interchangeable elements. Those skilled in the artwill understand that by increasing the number of elements of themass-customized femoral component, akin to stacking slices of thepatient's natural femur to reproduce this bone, one can increasinglyapproach the fit of a patient-specific implant by using mass-customizedelements.

Similar to the anatomical differences between genders and ethnicitiesfor the proximal femur, FIGS. 70-75 confirm that gender and ethnicdifferences exist across a general pelvis population within astatistical atlas. Referring back to FIG. 24, a series of masscustomized acetabular cup implants are designed and fabricated by usingstatistical atlas data (i.e., pelvis population) grouped based upon atleast one of gender and ethnicity. The grouped atlas data is subjectedto an automatic landmarking process and a surface/shape analysis processto isolate the geometry of the acetabular cup within the population, asdepicted graphically in FIG. 70. In addition, as depicted graphically inFIGS. 74 and 75, the landmarking (for location of acetabular ligament)and contour analysis (for evaluating the contours of the acetabular cup)processes lead to feature extraction, from which the anatomical cupimplant surfaces are ultimately generated, as shown in FIG. 71. Thisanalysis shows that the acetabular cup and femoral head are not composedof a single radius of curvature, but several radii, as shown in FIGS. 80and 81.

Creation of Animal-Specific Implants

Referring to FIG. 77, an exemplary system and methods for designing andfabricating an animal-specific (i.e., patient-specific for an animal)implant and associated instrumentation is similar to the processdepicted and explained previously with respect to FIG. 17, which isincorporated herein. As a prefatory matter, images of the animal anatomyare taken and automatically segmented to yield a virtual 3D bone model.Though graphically depicted as CT scan images, it should be understoodthat other imaging modalities besides CT may be utilized such as,without limitation, MRI, ultrasound, and X-ray. The virtual 3D bonemodel of the affected anatomy is loaded into the statistical atlas, inaccordance with the previous exemplary disclosure. Thereafter, inputsfrom the statistical atlas are utilized to reconstruct the bone(s) andcreate a reconstructed virtual 3D bone model. Bone landmarks arecalculated on the surface of the reconstructed virtual 3D bone model toallow determination of the correct implant size. Geometry of affectedbone is then mapped and converted to parametric form, which is then usedto create an animal-specific implant that mimics the residual anatomicalgeometry. In addition to the animal-specific implant, animal-specificinstrumentation is fabricated and utilized for preparation of theanimal's residual bone and placement of the animal-specific implant.

Referring to FIG. 78, an exemplary system and methods for designing andfabricating a mass customized animal implant is similar to the processdepicted and explained previously with respect to FIG. 24, which isincorporated herein. As a prefatory matter, 3D animal bone models fromthe statistical atlas pertinent to the bone(s) in question are subjectedto an automatic landmarking and surface/shape analysis. The automaticlandmarking process uses information stored in the atlas (e.g., regionslikely to contain a specific landmark) and local geometrical analyses toautomatically calculate anatomical landmarks for each 3D animal bonemodel. For each animal bone in question within the statistical atlas,the shape/surface analysis directly extracts features the surfacegeometry of the 3D virtual animal bone models. Thereafter, each of the3D animal bone models have a feature extraction process carried outthereon that uses a combination of landmarks and shape features tocalculate features relevant to implant design. These features are usedas inputs to a clustering process, where the animal bone population isdivided into groups having similar features using a predeterminedclustering methodology. Each resulting cluster represents thoseinstances used to define the shape and size of a single animal implant.A parameterization process follows for each cluster center (implantsize) in order to extract the parameters for an overall implant model(e.g., computer aided design (CAD) parameters). Thereafter, using theextracted parameters, the overall implant surface and size are generatedfor each cluster. Depending upon the cluster the animal patient fallsinto, the mass-customized implant is selected from the requisite groupand implanted.

Creation of Patient-Specific Cutting Guides

Referring to FIGS. 79-94, an exemplary process and system are describedfor integration of multidimensional medical imaging, computer aideddesign (CAD), and computer graphics features for designingpatient-specific cutting guides. For purposes of exemplary explanationonly, the patient-specific cutting guides are described in the contextof a total hip arthroplasty procedure. Nevertheless, those skilled inthe art will realize that the exemplary process and system areapplicable to any surgical procedure for which cutting guides may beutilized.

As represented in FIG. 79, an overview of the exemplary system flowbegins with receiving input data representative of an anatomy. Inputanatomical data comprises two dimensional (2D) images or threedimensional (3D) surface representations of the anatomy in question thatmay, for example, be in the form of a surface model or point cloud. Incircumstances where 2D images are utilized, these 2D images are utilizedto construct a 3D surface representation of the anatomy in question.Those skilled in the art are familiar with utilizing 2D images ofanatomy to construct a 3D surface representation. Accordingly, adetailed explanation of this process has been omitted in furtherance ofbrevity. By way of example, input anatomical data may comprise one ormore of X-rays (taken from at least two views), computed tomography (CT)scans, magnetic resonance images (MRIs), or any other imaging data fromwhich a 3D surface representation may be generated. In exemplary form,the anatomy comprises a pelvis and a femur.

It should be understood, however, that the following is an exemplarydescription of anatomies that may be used with the exemplary system andin no way is intended to limit other anatomies from being used with thepresent system. As used herein, tissue includes bone, muscle, ligaments,tendons, and any other definite kind of structural material with aspecific function in a multicellular organism. Consequently, when theexemplary system and methods are discussed in the context of bonesinvolved with the hip joint, those skilled in the art will realize theapplicability of the system and methods to other tissue.

The femur and pelvis input anatomy data of the system is directed to oneof two modules depending upon the type of input data. In the case ofX-ray data, the 2D X-ray images are input to a non-rigid module in orderto extract 3d bone contours. If the input data is in the form of CTscans or MRI images, these scans/images are directed to an autosegmentation module where the scans/images are automatically segmentedto extract the 3D bone contours (and 3D cartilage contours).

Referring to FIG. 80, the non-rigid module uses the multiple X-rayimages taken from at least two different views are subjected to one ormore pre-processing steps. These steps may include one or more of thefollowing: noise reduction and image enhancement. The resultantpre-processed X-ray images are subjected to a calibration step in orderto register the X-ray images. Preferably, the X-ray images have beentaken in the presence of a fixed position calibration device so that theX-ray images are registered with respect to this fixed positioncalibration device. But when no fixed position calibration device ispresent in the X-ray images, the images may nonetheless be calibratedusing common detected features across multiple images. From thiscalibration process, the output is the position of the anatomy relativeto the imager, which is identified by the “Pose” reference in FIG. 80.

The resultant pre-processed X-ray images are subjected to a featureextraction step. This feature extraction step comprises one or morecomputations of image features utilizing the pre-processed X-ray images.By way of example, these computations may include gradient features,contours, textural components, or any other image derived feature. Inthis exemplary process, the feature extraction step outputs the outlineof the anatomy (e.g., bone shape) as represented by the “Contour”reference in FIG. 80, as well as image features as represented by the“Texture” reference, derived from the X-ray images. Both the outlinedanatomy and image feature data is directed to a non-rigid registrationstep.

The non-rigid registration step registers the outputs from the featureextraction step and the calibration step to a 3D template model of theanatomy in question from a statistical atlas. By way of example, the 3Dtemplate model is generated responsive to non-linear principalcomponents from an anatomical database comprising part of thestatistical atlas. During the non-rigid registration step, the 3Dtemplate model has its shape parameters (non-linear principalcomponents) optimized to match the shape parameters of the X-ray imagesresulting from the pose, contour, and texture data. The output from thenon-rigid registration step is a 3D patient-specific bone model, whichis directed to a virtual templating module, similar to the 3Dpatient-specific bone model output from the auto segmentation module forCT scans or MRI images.

Referencing FIG. 83, the auto segmentation process is initialized bytaking the CT scans or MRI images, for example, and carrying out anautomatic segmentation sequence. With specific reference to FIG. 82, theautomatic segmentation sequence includes aligning the scans/images withrespect to a base or starting 3D model of the anatomy in question. Afteralignment of the scans/images to the base 3D model, the scans/images areprocessed via an initial deformation process to calculate the normalvectors, determine locations of the profile points, linearly interpolatethe intensity values, filter the resulting profiles using aSavitsky-Golay filter, generate a gradient of the profiles, weigh theprofiles using a Gaussian weight profile equation, determine the maximumprofiles, and use these maximum profiles to deform the base 3D model.The resulting deformed 3D model is projected onto the template 3D modelfrom a statistical atlas for the anatomy in question. Using theparameters of the template 3D model, the deformed 3D model is furtherdeformed in a secondary deformation process to resemble features uniqueo the template 3D model. After this latter deformation process, thedeformed 3D model is compared to the scans/images to discern whethersignificant differences exist.

In circumstances where significant differences exist between thedeformed 3D model and the scans/images, the deformed 3D model and thescans/images are again subjected to the initial deformation processfollowed by the secondary deformation process. This looping process iscontinued until the deformed 3D model is within a predeterminedtolerance(s) for differences between the deformed 3D model and thescans/images.

After the deformed 3D model has been determined to exhibit less thansignificant differences with respect to the previous iteration or amaximum number of iterations is achieved, the surface edges of thedeformed 3D model as smoothed, followed by a higher resolution remeshingstep to further smooth the surfaces to create a smoothed 3D model. Thissmoothed 3D model is subjected to an initial deformation sequence(identical to the foregoing initial deformation process prior to surfacesmoothing) to generate a 3D segmented bone model.

Referring back to FIG. 83, the 3D segmented bone model is processed togenerate contours. In particular, the intersection of the 3D segmentedbone model and the scans/images are calculated, which result in binarycontours at each image/scan plane.

The 3D segmented bone model is also processed to generate a statistical3D model of the bone appearance that is patient-specific. In particular,the appearance of the bone and any anatomical abnormality is modeledbased on image information present in within the contours and externalto the contours.

The bone contours are thereafter reviewed by a user of the segmentationsystem. This user may be a segmentation expert or infrequent user of thesegmentation system that notices one or more areas of the 3D model thatdo not correlate with the segmented regions. This lack of correlationmay exist in the context of a missing region or a region that is clearlyinaccurate. Upon identification of one or more erroneous regions, theuser may select a “seed point” on the model indicating the center of thearea where the erroneous region exists, or manually outlines the missingregions. The software of the system uses the seed point to add orsubtract from the contour local to the seed point using the initialscans/images of the anatomy from CT or MRI. For example, a user couldselect a region where an osteophyte should be present and the softwarewill compare the scans/images to the region on the 3D model in order toadd the osteophyte to the segmentation sequence. Any changes made to the3D model are ultimately reviewed by the user and verified or undone.This review and revision sequence may be repeated as many times asnecessary to account for anatomical differences between the scans/imagesand the 3D model. When the user is satisfied with the 3D model, theresulting model may be manually manipulated to remove bridges and touchup areas of the model as necessary prior to being output to the virtualtemplating module.

As shown in FIGS. 79 and 84, the virtual templating module receives 3Dpatient-specific models from either or both the auto segmentation moduleand the non-rigid registration module. In the context of a hip joint,the 3D patient-specific models include the pelvis and the femur, whichare both input to an automatic landmarking process. This automaticlandmarking step calculates anatomical landmarks relevant to implantplacement on the femur and pelvis 3D models using regions from similaranatomy present in a statistical atlas and local geometrical searches.

In the context of automatic placement of the femoral stem using distalfixation, as shown in FIG. 85, the automatic landmarking includesdefinition of axes on the femur and the implant. With respect to thefemur, the anatomical femoral axis (AFA) is calculated, followed by theproximal anatomical axis (PAA). The proximal neck angle (PNA) is thencalculated, which is defined as the angle between the AFA and PNA. Withrespect to the femoral implant, the implant axis is along the length ofthe implant stem and the implant neck axis is along the length of theimplant neck. Similar to the PNA of the femur, the implant angle isdefined as the angle between the implant axis and the implant neck axis.The implant is then chosen which has an implant angle that is closest tothe PNA. The implant fitting angle (IFA) is then defined as theintersection of the proximal anatomical axis with a vector drawn fromthe femoral head center at the chosen implant angle.

When using automatic placement of the femoral stem using distal fixationand the calculated anatomical landmarks, as shown in FIG. 85, an implantsizing step determines/estimates for the appropriate implant sizes forfemoral components. The implant size is chosen by comparing the width ofthe implant to the width of the intramedullary canal and selecting theimplant with the most similar width to the intramedullary canal.Thereafter, the system moves forward to an implant placement step.

In the implant placement step for a distal fixation femoral stem, basedon surgeon preferred surgical technique and previously calculatedanatomical landmarks, the initial implant position is determined/chosenfor all relevant implanted components. A resection plane is created tosimulate the proximal femur osteotomy and the implant fit is assessed.Fit assessment is conducted by analyzing the cross sections of thealigned implant and femur intramedullary canal at varying levels alongthe implant axis. The implant is aligned to the femur by aligning theimplant axis to the anatomic femur axis then translating the implant sothat the neck of the implant is in the general location of the proximalfemur neck. The implant is then rotated about the anatomic femur axis toachieve desired anteversion.

As part of this implant placement step, an iterative scheme is utilizedthat includes using an initial “educated guess” as to implant placementas part of a kinematic simulation to evaluate the placement of the“educated guess.” In exemplary form, the kinematic simulation takes theimplant (based upon the placement of the implant chosen) through a rangeof motion using estimated or measured joint kinematics. Consequently,the kinematic simulation may be used to determine impingement locationsand estimate the resulting range of motion of the implant postimplantation. In cases where the kinematic simulation results inunsatisfactory data (e.g., unsatisfactory range of motion,unsatisfactory mimicking of natural kinematics, etc.), another locationfor implant placement may be utilized, followed by a kinematic analysis,to further refine the implant placement until reaching a satisfactoryresult. After the implant position is determined/chosen for all relevantimplanted components, the template data is forwarded to a jig generationmodule.

In the context of automatic placement of the femoral stem using pressfit and three contacts, as shown in FIG. 86, the automatic landmarkingincludes definition of axes on the femur and the implant. With respectto the femur, the anatomical femoral axis (AFA) is calculated, followedby the proximal anatomical axis (PAA). The proximal neck angle (PNA) isthen calculated, which is defined as the angle between the AFA and PNA.With respect to the femoral implant, the implant axis is along thelength of the implant stem and the implant neck axis is along the lengthof the implant neck. Similar to the PNA of the femur, the implant angleis defined as the angle between the implant axis and the implant neckaxis. The implant is then chosen which has an implant angle that isclosest to the PNA. The implant fitting angle (IFA) is then defined asthe intersection of the proximal anatomical axis with a vector drawnfrom the femoral head center at the chosen implant angle.

When using automatic placement of the femoral stem using press fit,three contacts, and the calculated anatomical landmarks, as shown inFIG. 86, an implant sizing step determines/estimates for the appropriateimplant sizes for pelvis and femoral components. The implant size ischosen by aligning the implant to the femur by aligning the implant axisto the anatomic femur axis. The implant is then rotated to align itsneck axis with the femoral neck axis. The implant is then translated tobe in an anatomically proper position within the proximal femur.Thereafter, the system moves forward to an implant placement step.

In the implant placement step for a press fit femoral stem, based onsurgeon preferred surgical technique and previously calculatedanatomical landmarks, the initial implant position is determined/chosenfor all relevant implanted components. A resection plane is created tosimulate the proximal femur osteotomy and the implant fit is assessed.Fit assessment is conducted by analyzing a contour of the implant andfemur intramedullary canal. The contour is created by intersecting theintramedullary canal with a plane normal to both anatomical axis andfemoral neck axis, passing through the point of intersection of theanatomical axis and femur neck axis, producing a contour. When theimplant and intramedullary canal contours are generated, only theimplants with widths less than the intramedullary canal width at thesame location are kept, resulting in many possible correct implantsizes. The group of possible sizes is reduced through two strategiesreducing mean square distance error between the implant and theintramedullary canal. The first strategy minimizes the mean square error(MSE) or other mathematical error metric of the distance between bothmedial and lateral sides of the implant and the intramedullary canal.The second strategy minimizes the MSE of the distance between thelateral side of the implant and the intramedullary canal.

As part of this implant placement step, an iterative scheme is utilizedthat includes using an initial “educated guess” as to implant placementas part of a kinematic simulation to evaluate the placement of the“educated guess.” In exemplary form, the kinematic simulation takes theimplant (based upon the placement of the implant chosen) through a rangeof motion using estimated or measured joint kinematics. Consequently,the kinematic simulation may be used to determine impingement locationsand estimate the resulting range of motion of the implant postimplantation. In cases where the kinematic simulation results inunsatisfactory data (e.g., unsatisfactory range of motion,unsatisfactory mimicking of natural kinematics, etc.), another locationfor implant placement may be utilized, followed by a kinematic analysis,to further refine the implant placement until reaching a satisfactoryresult. After the implant position is determined/chosen for all relevantimplanted components, the template data is forwarded to a jig generationmodule.

Referring back to FIG. 79, the jig generation module generates apatient-specific guide model. More specifically, from the template dataand associated planning parameters, the shape and placement of apatient-specific implant is known with respect to the patient's residualbone. Consequently, the virtual templating module, using thepatient-specific 3D bone model, calculates the position of the implantwith respect to the patient's residual bone and, thus, provides the jiggeneration module with information as to how much of the patient'sresidual bone is intended to be retained. Consistent with this boneretention data, the jig generation module utilizes the bone retentiondata to assign one or more bone cuts to reduce the patient's currentbone to the residual bone necessary to accept the implant as planned.Using the intended bone cut(s), the jig generation module generates avirtual 3D model of a cutting guide/jig having a shape configured tomate with the patient's bone in a single location and orientation. Inother words, the 3D model of the cutting jig is created as a “negative”of the anatomical surface of the patient's residual bone so that thetangible cutting guide precisely matches the patient anatomy. In thisfashion, any guesswork associated with positioning of the cutting jig iseliminated. After the jig generation module generates the virtual 3Dmodel of the cutting jig, the module outputs machine code necessary fora rapid prototyping machine, CNC machine, or similar device to fabricatea tangible cutting guide. By way of example, the exemplary cutting jigfor resection of the femoral head and neck comprises a hollow slot thatforms an associated guide to constrain a cutting blade within a certainrange of motion and maintains the cutting blade at a predeterminedorientation that replicates the virtual cuts from the surgical planningand templating modules. The jig generation module is also utilized tocreate a placement jig for the femoral stem.

Referring to FIG. 92, subsequent to resecting the femoral head and neck,intramedullary reaming followed by femoral stem insertion takes place.In order to prepare the femur for insertion of the femoral implant,reaming of the intramedullary canal needs to take place along anorientation consistent with the orientation of the femoral implant. Ifthe reaming is offset, the orientation of the femoral implant may becompromised. To address this concern, the jig generation modulegenerates a virtual guide that is a “negative” of the anatomical surfaceof the patient's residual or resected bone so that a rapid prototypingmachine, CNC machine, or similar device can fabricate the cutting guidethat precisely matches the patient anatomy. By way of example, thereaming jig may include an axial guide along which the reamer maylongitudinally traverse. Using this reaming jig, the surgeon performingthe reaming operation is ensured of reaming in the proper orientation.

The intramedullary canal may receive the femoral stem. Again, to ensurethe femoral stem is properly positioned both from a rotationalperspective and an angular perspective within the intramedullary canal,the jig generation module generates a femoral stem placement guide. Byway of example, the femoral stem placement guide concurrently is a“negative” of the anatomical surface of the patient's residual orresected bone as well as the top of the femoral stem. In this manner,the placement guide slides over the femoral shaft (portion of femoralstem that the femoral ball is connected to) and concurrently includes aunique shape to interface with the patient's residual or resected boneso that only a single orientation of the femoral stem is possible withrespect to the patient's femur, thereby ensuring proper implantation ofthe femoral stem consistent with pre-operative planning. It should benoted, however, that while the exemplary jigs have been described in thecontext of a primary hip implant, those skilled in the art shouldunderstand that the foregoing exemplary process and system are notlimited to primary hip implants or limited to hip implant or revisionsurgical procedures. Instead, the process and system are applicable toany hip implants in addition to surgical procedures involving otherareas of the body including, without limitation, knee, ankle, shoulder,spine, head, and elbow.

As depicted in FIG. 93, in the context of the acetabulum, the jiggeneration module may generate instructions for fabricating reaming andacetabular implant placement guides for the acetabular cup. Inparticular, from the template data and associated planning parameters,the shape and placement of a patient-specific acetabular implant isknown with respect to the patient's residual pelvis. Consequently, thevirtual templating module, using the patient-specific 3D acetabulummodel, calculates the size and position of the acetabular cup implantwith respect to the patient's residual bone and, thus, provides the jiggeneration module with information as to how much of the patient'sresidual pelvis is intended to be retained and the desired implantorientation. Consistent with this bone retention data, the jiggeneration module utilizes the bone retention data to assign one or morebone cuts/reaming to reduce the patient's current pelvis to the residualbone necessary to accept the acetabular implant as planned. Using theintended bone cut(s), the jig generation module generates a virtual 3Dmodel of a cutting guide/jig having a shape configured to mate with twoportions of the patient's pelvis via only one orientation. In otherwords, the 3D model of the cutting jig is created as a “negative” of theanatomical surface of the patient's pelvis so that the tangible reamingguide precisely matches the patient anatomy. In this fashion, anyguesswork associated with positioning of the reaming jig is eliminated.After the jig generation module generates the virtual 3D model of thereaming jig, the module outputs machine code necessary for a rapidprototyping machine, CNC machine, or similar device to fabricate atangible reaming jig. By way of example, the exemplary acetabularcomponent jig for reaming the acetabulum comprises a four-piecestructure, where a first piece is configured to be received in thenative acetabulum and temporarily mount to the second piece until thesecond piece is secured to the pelvis using the first piece as aplacement guide. After the second piece is fastened to the pelvis, thefirst piece may be removed. Thereafter, the third piece includes acylindrical or partially cylindrical component that uniquely interfaceswith the second piece to ensure the reamer can longitudinally traversewith respect to the third piece, but its orientation is fixed using acombination of the first and third pieces. Following reaming, the reameris removed and the third piece is removed from the first piece. Theacetabular cup implant is mounted to the reamed acetabulum using a forthpiece. In particular, the fourth piece is shaped uniquely to engage thefirst piece in only a single orientation, while at the same time beingformed to be received within the interior of the acetabular cup implant.After the implant cup is positioned, both the first and fourth piecesare removed. It should also be noted that additional jigs may be createdfor drilling one or more holes into the pelvis to seat the acetabularimplant, where each drilling jig is mounted in succession to the firstpiece in order to verify the orientation of the drill bit.

Creation of Trauma Plates

Referring to FIGS. 95-108, an exemplary process and system are describedfor creating bone plates (i.e., trauma plates) across a predeterminedpopulation. Those skilled in the art are aware that bone is able toundergo regeneration to repair itself subsequent to a fracture.Depending on the severity and location of the fracture, prior art traumaplates were utilized that often required bending or other modificationsin the operating room to conform to an irregular bone shape and achievemaximum contact between the bone fragments. However, excessive bendingdecreases the service life of the trauma plate, which may lead to boneplate failure and/or trauma plate-screw fixation loosening. The instantprocess and system provides a more accurate trauma plate shape to reduceor eliminate having to contour the plate interoperatively, therebyincreasing plate service life and increasing the time until any boneplate-screw fixation loosening occurs.

The foregoing exemplary explanation for creating trauma plates isapplicable to any and all bones for which trauma plates may be applied.For purposes of brevity, the exemplary explanation describes the systemand process for creation of a trauma plate for use with the humerusbone. But it should be understood that the process and system is equallyapplicable to other bones of the body and fabrication of correspondingtrauma plates and is in no way restricted to humerus trauma plates.

As part of the exemplary process and system for creating trauma plates,a statistical bone atlas is created and/or utilized for the bone(s) inquestion. By way of explanation, the bone in question comprises ahumerus. Those skilled in the art are familiar with statistical atlasesand how to construct a statistical atlas in the context of one or morebones. Consequently, a detailed discussion of constructing thestatistical bone atlas has been omitted in furtherance of brevity.Nevertheless, what may be unique as to the statistical bone atlas of theexemplary system and process is categorizing humeri within thestatistical bone atlas based upon gender, age, ethnicity, deformation,and/or partial construction. In this fashion, one or more trauma platesmay be mass customized to one or more of the foregoing categories, wherethe one or more categories establish a particular bone population.

In exemplary form, the statistical bone atlas includes anatomical datathat may be in various forms. By way of example, the statistical boneatlas may include two dimensional or three dimensional images, as wellas information as to bone parameters from which measurements may betaken. Exemplary atlas input data may be in the form of X-ray images, CTscan images, MRI images, laser scanned images, ultrasound images,segmented bones, physical measurement data, and any other informationfrom which bone models may be created. This input data is utilized bysoftware accessing the statistical atlas data to construct threedimensional bone models (or access three dimensional bone models havingalready been created and saved as part of the statistical atlas), fromwhich the software is operative to create a mean bone model or templatebone model in three dimensions.

Using the template bone model, the software can automatically designateor allows manual designation of points upon the exterior surface of thetemplate bone model. By way of explanation, in the context of the meanhumerus model, a user of the software establishes a general boundaryshape for the eventual trauma plate by generally outlining the shape ofthe trauma plate on the exterior surface of the humerus model. Thegeneral boundary shape of the trauma plate can also be accomplished bythe user designating a series of points on the exterior surface of thehumerus model that correspond to an outer boundary. Once the outerboundary or boundary points are established, the software mayautomatically designate or allows manual designation of points on theexterior surface of the humerus model within the established boundary.By way of example, the software provides a percent fill operation uponwhich the user can designate that percentage within the boundary of thetrauma plate to be designated by a series of points, each correspondingto a distinct location on the exterior of the humerus model. Inaddition, the software provides a manual point designation feature uponwhich the user may designate one or more points upon the exteriorsurface of the humerus model within the boundary. It should be notedthat in cases where manual point designation is utilized, the user neednot establish a boundary as a prefatory matter to designating pointsupon the exterior of the humerus model. Rather, when the manualdesignation of points is completed, the boundary is established by theoutermost points designated.

After the designation of points on the exterior surface of the templatebone model, the localized points are propagated throughout the bonepopulation in question. In particular, the localized points areautomatically applied to each three dimensional bone model within thegiven population by the software via point correspondence of thestatistical atlas. By way of example, the given bone population may begender and ethnic specific to comprise humeri from Caucasian women.Using the propagated points for each bone model of the population, thesoftware fills in the voids between points within the boundary using athree dimensional filling process to create a three dimensionalrendering of the trauma plate for each bone. Thereafter, the softwarecalculates the longitudinal midline of the three dimensional renderingof each trauma plate via a thinning process.

The midline of each three dimensional trauma plate rendering comprises athree dimensional midline having various curvatures along the lengththereof. The software extracts the three dimensional midline and, usinga least square fitting, determines the preferred number of radii ofcurvature that cooperatively best approximate the predominant curvatureof the three dimensional midline. In the context of humeri, it has beendetermined that three radii of curvature accurately approximate themidline curvature. But this number may vary depending upon the bonepopulation and the boundary of the trauma plate. Additional features canbe included here as well, such as cross-sectional curvature at one ormore locations along the length of the plate, location of muscles,nerves and other soft tissues to avoid, or any other feature relevant todefining plate size or shape. By way of example, the three radii ofcurvature for the midline represent the bend in the trauma plate in theproximal humerus, the transition between the humeral shaft and thehumeral head, and the curvature of the humeral shaft. Each radii ofcurvature is recorded and a four dimensional feature vector was appliedto the radii of curvature data to cluster the radii into groups thatbest fit the population. In exemplary form, the cluster data mayindicate that multiple trauma plates are necessary to properly fit thepopulation. Once the radii of curvature data is clustered, the traumaplate dimensions may be finalized.

Upon feature extraction related to the plate design, the softwaredetermines the best number of clusters that fits the population. It mustbe noted that there are some instances where there are two or moreclusters that provide local minima as outlined in FIG. 100. In order todetermine the optimum choice that provides acceptable error tolerance aswell as reasonable number of plates in each family, the softwaregenerates three dimensional surface model for the plates in eachclusters. Automatic evaluation is then performed by placing those plateson the population and computing the mismatch between the plate and thebone surface. Results of this analysis allow the software to pick theoptimal number of plates to be used for this specific population. Thefinal plate models are then parameterized and screw locations are placedon each plate in such a fashion as to avoid muscle and soft tissuelocations as well as maximize fixation. The width of the screws aredetermined by the cross sectional analysis of the bone at each screwlevel across the population.

The instant process and method was validated for the humerus using acadaver study. In particular, CT scans were taken of cadaver humerusbones from Caucasian white females. These CT scans were utilized by thesoftware to create separate three dimensional models for each humeri. Itshould be noted that neither the CT scans nor the three dimensionalmodels utilized during this validation study were part of thestatistical atlas and relevant population utilized to create the humeraltrauma plates. Consequently, the CT scans nor the three dimensionalmodels comprised new data and models used to validate the humeral traumaplates designed. After the three dimensional validation models had beengenerated, each of the models was categorized to a particular cluster(the clusters resulting from designing the humeral trauma plate from thedesign population). Based upon which cluster the validation model wascategorized to, the designed humeral trauma plate for that cluster wasfitted to the appropriate validation three dimensional humeral bonemodel and measurements were calculated showing any spacing between theexterior surface of the validation three dimensional humeral bone modeland the underside surface of the humeral trauma plate. FIG. 106 depictsa distance map of the trauma plate fitted upon to the validation threedimensional humeral bone model to show areas of maximum distance betweenthe bone and trauma plate. It can be seen that a majority of the traumaplate is minimally spaced from the bone, while areas of less conformityonly show spacing that ranges between 0.06 and 0.09 centimeters.Consequently, it was determined at the conclusion of this cadaver studythat the trauma plates designed pursuant to the foregoing exemplaryprocess using the foregoing system had extraordinary contour matchingthat, when applied intraoperatively, obviated the practice of surgeonshaving to bend or manually reshape bone plates.

In another exemplary instance of this process, trauma plates werecreated for the clavicle. Here, a statistical atlas was created fromseveral clavicle bones, which sufficiently captured the variation withinCaucasian population. Additionally, defined within the statistical atlaswere locations relating to muscle attachment sites. Cross-sectionalcontours were extracted at 5% increments along the entire bone, as wellas at muscle attachment sites and at the clavicle waist. Maximum andminimum dimensions of each cross-sectional contour were calculated. Inaddition, the entire three-dimensional surface was examined forasymmetry by analyzing the magnitude and directional differences betweenhomologous points across all bone surfaces in the dataset. The resultsconfirm the existing studies on clavicle asymmetry, namely that the leftclavicle is longer than the right, but the right is thicker than theleft. However, the patterns of asymmetry differ between males andfemales. Additionally, the clavicle midline does not follow asymmetrical “S” shape, as in existing plate designs. Males aresignificantly asymmetric in all dimensions and at muscle and ligamentattachment site contours (p<0.05), whereas female asymmetry is morevariable. We hypothesize that this has to do with the absolute andrelative differences in male muscle strength compared to females.However, an area with no muscle attachments on the posterior midshaftwas significantly asymmetric in both sexes. From the extracted features,clustering was performed to find the family of clavicle plates tooptimally fit the population. Additionally, screw fixation locations andlength can be determined to optimally avoid soft tissues (muscleattachments) and prevent additional fractures or plate loosening as aresult of screws which are too long or too short. Using the process,several plate families were designed, as seen in FIG. 110, FIG. 111,FIG. 112, FIG. 113 and FIG. 114.

Creation of Trauma Plate Placement Guides

Referring to FIG. 115, an exemplary process and system are described forcreating trauma plate placement guides that are patient-specific. Thoseskilled in the art are aware that bone can fracture at one or morelocations resulting in bone fragments that are separated from oneanother. As part of reconstructive surgery to repair the bone, thesefragments are held in a fixed orientation using one or more traumaplates. Reconstructive surgeons attempted to piece the bone backtogether using innate knowledge rather than patient-specific anatomicalfact. Consequently, to the extent patient bone anatomy varied fromnormal, the bone fragments were grossly distorted, or the number of bonefragments was large, surgeons would resort to using prior art traumaplates and having the bone fragments match the shape of the plate ratherthan vice versa. The instant process and system improves upon prior arttrauma plate application by creation of trauma plate placement guidesand customized trauma plates that match the trauma plates to the bone toreplicate the original bone shape and orientation.

The exemplary system flow begins with receiving input datarepresentative of a fractured anatomy. For purposes of explanation only,the fractured anatomy comprises a human skull. It should be noted thatthe foregoing process and system is equally applicable to otheranatomies/bones including, without limitation, bones in the arms, legs,and torso. In exemplary form, anatomy data input may be in the form ofX-rays, CT scans, MRIs, or any other imaging data from which bone sizeand shape may be represented.

The input anatomy data is utilized to construct a three dimensionalvirtual model of the fractured anatomy. By way of example, the inputanatomy data comprises a computed tomography scan of a fractured skullthat is processed by software to segment this scan and generate a threedimensional model. Those skilled in the art are familiar with how toutilize computed tomography scans to construct three dimensional virtualmodels. Consequently, a detailed description of this aspect of theprocess has been omitted in furtherance of brevity.

Subsequent to generation of the three dimensional virtual model of thefractured skull, the software compares the three dimensional virtualmodel of the skull with data from a statistical atlas to determine areasin the three dimensional virtual model where the skull is fractured. Inparticular, the software utilizes features extracted from the surfacemodel of the input anatomy (ex: surface roughness, curvature, shapeindex, curvedness, neighbor connectivity) to extract areas of fracturesites. The outline contours of those fracture sites are then extractedand matched together to find the matching fracture sites. Fracturedfragments are also matched with the atlas to indicate the best locationto place the matched fracture sites in order to reconstruct the normalanatomy.

After the software generates a reconstructed three dimensional virtualmodel of the fractured skull, buttresses may be manually and/orautomatically positioned on the exterior of the reconstructed threedimensional virtual skull model. The automatic placement of thebuttresses is the result of programmed logic to maximize stability ofthe bone fragments while minimizing the number of buttresses. As usedherein, the term buttress and plurals thereof refer to any support usedto steady bone fragments with respect to one another. In certaininstances, practical experience by a surgeon or other learned user maysupplement or supplant to the logic when making use of the manualbuttress placement feature. In any event, a series of buttresses areprogrammed into the software that allows the software or a user of thesoftware to select differing buttresses for differing applications. Atthe same time, the length of the buttresses may be manually orautomatically manipulated based upon the dimensions of the fracture andbone fragments.

Subsequent to buttress assignment and placement on the reconstructedthree dimensional virtual skull model, the software dimensions andcontour of each buttress is recorded by the software. This recordationincludes information necessary for fabrication of each buttress or atthe very least information helpful to allow a surgeon or other learnedindividual to take existing buttresses and conform each to a placementguide. In the context of molding an existing buttress, the softwareextracts the contours of the reconstructed three dimensional virtualskull model to generate computer-aided design (CAD) instructions forcreation of one or more tangible models indicative of the reconstructedthree dimensional skull model. These CAD instructions are sent to arapid prototyping machine, which creates the one or more tangible modelsindicative of the reconstructed three dimensional skull model. Byrecreating the proper anatomical surface as a tangible model, eachbuttress may be applied to the tangible model at the target location andmanually conformed prior to implantation and fastening to the patient'sskull.

Based upon the location and length of any buttress, the software alsoextracts the contours of the reconstructed three dimensional virtualskull model to generate contour data for one or more patient-specificbuttress placement guides. In particular, a placement guide may begenerated for each buttress. In this manner, the placement guideincludes a surface contour that matches the contour of the patient'sskull in a single orientation. Given that the location of the buttressis known on the virtual model of the reconstructed skull, as is thecontour of the adjacent exterior skull surface, the software combinesthe two to create a virtual patient-specific placement guide. Thisvirtual guide is output in the form of CAD instructions to a rapidprototyping machine for fabrication.

In this exemplary embodiment, the fabricated patient-specific placementguide comprises an elongated handle configured to be gripped by asurgeon. Extending from the end of the elongated handle is a blockC-shaped contour plate. The underside of the contour plate is concave tomatch the convex topography of the skull at the location where thebuttress should be positioned. Though not required, the ends (or anotherportion) of the contour plate may be fastened to the buttress, or thecontour plate may simple provide a working window within which thebuttress is aligned and ultimately fastened to the skull. Postattachment of the buttress to the skull, the contour plate may beremoved.

Customized Cutting & Placement Guides, Plates

Referring to FIG. 116, reconstruction of a deformed, fractured, orpartial anatomy is one of the complex problems facing healthcareproviders. Abnormal anatomy may be the result of birth conditions,tumors, diseases, or personal injuries. As part of providing treatmentfor various ailments, healthcare providers may find it advantageous toreconstruct an anatomy or construct an anatomy to facilitate treatmentfor various conditions that may include, without limitation,broken/shattered bones, bone degeneration, orthopedic implant revision,orthopedic initial implantation, and disease.

The present disclosure provides a system and methods for bone and tissuereconstruction using bone grafts. In order to carry out thisreconstruction, the system and associated methods utilizes currentanatomy images of a patient to construct two virtual 3D models: (a) afirst 3D model representative of the current abnormal anatomy; and, (2)a second 3D model representative of the reconstructed anatomy of thepatient. Reference is had to the prior “Full Anatomy Reconstruction”section for a detailed explanation of using patient images (X-rays, CTscans, MRI images, etc.) to arrive at virtual models of the patient'sabnormal anatomy and reconstructed anatomy. The present system andmethods builds upon the system described in the “Full AnatomyReconstruction” section to utilize the two 3D virtual models incombination with constructing a 3D virtual model of one or more bonesfrom which a bone graft may be taken (i.e., a donor bone). As will bedescribed in more detail hereafter, the 3D virtual models of thepatient's reconstructed and abnormal anatomy are analyzed to generate a3D virtual model of the bone graft needed for reconstruction. This 3Dvirtual graft model is compared to the 3D virtual model of the donorbone to access one or more sites on the donor bone from which a bonegraft can be excised. After determining the excise location(s), cuttingguides and graft placement guides are designed and fabricated forgathering the grafted bone and mounting the grafted bone to the site ofreconstruction.

By way of exemplary explanation, the instant system and methods will bedescribed in the context of a facial reconstruction, where the donorbone comprises the fibula. Those skilled in the art should realize thatthe instant system and methods are applicable to any reconstructivesurgical procedure utilizing one or more bone grafts. Moreover, whilediscussing facial reconstruction and the fibula as the bone donor, thoseskilled in the art should understand that the exemplary system andmethods may be used with donor bones other than the fibula.

As a prefatory step to discussing the exemplary system and methods foruse with reconstructive surgical planning and surgical procedures usingbone grafts, it is presumed that the patient's abnormal anatomy has beenimaged and virtual 3D models of the patient's abnormal and reconstructedanatomy have been generated pursuant to those processes described in theprior “Full Anatomy Reconstruction” section. Consequently, a detaileddiscussion of utilizing patient images to generate both virtual 3Dmodels of the patient's abnormal and reconstructed anatomy has beenomitted in furtherance of brevity.

After virtual 3D models of the patient's abnormal and reconstructedanatomy have been created, the software compares the anatomies andhighlights areas of difference. In particular, the areas in commonbetween the virtual 3D models denotes bone that will be retained,whereas areas that differ is indicative of one or more sites forreconstruction. The software extracts from the virtual 3D model of thepatient's reconstructed anatomy those areas not in common and isolatesthese areas as separate 3D virtual models of the intended bone graft.The surgeon or other pre-operative planner may view the virtual 3D bonegraft models and use his judgment as to the bone or bones from which thebone grafts might be best excised.

Regardless as to the logic utilized to initially choose a possible boneas a graft candidate, the bone(s) in question is imaged usingconventional modalities (X-ray, CT, MRI, etc.). Using the processesdescribed in the prior “Full Anatomy Reconstruction” section, eachimaged bone is segmented and a virtual 3D model of the imaged bone iscreated. This 3D donor bone model is compared to the virtual 3D bonegraft model to isolate areas in common. In particular, the softwarecompares the surface contours of the 3D donor bone model with thesurface contours of the virtual 3D bone graft model to identify areas incommon or having similar curvature. Presuming no areas are in common orsimilar, the process can be restarted by analyzing another possibledonor bone. In contrast, if one or more areas in common or havingsimilar curvature exist in the donor bone, these areas are highlightedon the 3D donor bone model. In particular, the highlighted areas mimicthe shape of the virtual 3D bone graft model. If the area in common isjudged to be appropriate for excising the bone graft, the softwarevirtually excises the bone graft as a virtual 3D model and applies thebone graft (which has contours specific/unique as to the donor bone) tothe virtual 3D model of the patient's abnormal anatomy to verifypotential fit and any areas of the patient's abnormal anatomy that mayneed to be excised as part of the reconstruction. In circumstances whereapplication of the virtual 3D model of the excised bone to the virtual3D model of the patient's abnormal anatomy results less thansatisfactory reconstruction, the process may be restarted at the boneselection point or restarted to excise a different area of bone. Butpresuming application of the virtual 3D model of the excised bone to thevirtual 3D model of the patient's abnormal anatomy results in anappropriate fit, the system moves forward with designing jigs tofacilitate excising the bone graft and mounting the bone graft to thepatient's residual bone.

In this exemplary embodiment, the system generates and outputs machinecode necessary for a rapid prototyping machine, CNC machine, or similardevice to fabricate a bone graft cutting guide and a bone graftplacement guide. In order to generate the outputs necessary to fabricatethe bone graft cutting guide and a bone graft placement guide, thesystem utilizes the virtual 3D model of the excised bone to the virtual3D model of the patient's abnormal anatomy.

In particular, the virtual 3D model of the excised bone defines theboundary of a virtual 3D cutting guide. Moreover, in this exemplarycontext, a portion of the fibula is intended to be excised to providethe bone graft. In order to ensure the appropriate portion of the fibulais excised, the virtual 3D cutting guide includes a window within whicha cutting device (saw, cutting drill, etc.) traverses to create theappropriately outlined bone graft. Not only does the virtual 3D cuttingguide need to be shaped to create the appropriate bone graft outline,but it also needs to be shaped to ensure placement of the cutting guideon the patient's donor bone is particularized. More specifically, theplacement of the cutting guide on the donor bones needs to concurrentlyensure the excised bone includes the correct outline shape and alsoexhibits the correct contours. In this fashion, the underside of thevirtual 3D cutting guide is designed to be the “negative” of the surfaceof the donor bone where the cutting guide will be mounted. Exemplarymounting techniques for securing the cutting guide to the donor bone mayinclude, without limitation, screws, dowels, and pins. In order toaccommodate one or more of these mounting techniques or others, thevirtual 3D cutting guide is also designed to include one or more throughorifices besides the window within which the surgical cutter traverses.After the design of the virtual 3D cutting guide is completed, thesystem generates and outputs machine code necessary for a rapidprototyping machine, CNC machine, or similar device to fabricate thebone graft cutting guide, which is followed by fabrication of the actualcutting guide.

In addition to the cutting guide, the software also designs one or morebone graft placement guides. The bone graft placement guides arepatient-specific and conform to the anatomy of the patient (both donorbone and residual bone to which the donor bone is mounted) to ensurecorrect placement of the bone graft with respect to the residual bone.In exemplary form, the bone graft placement guide is configured for amandible bone reconstructive procedure. In order to design the bonegraft placement guides, the software utilizes the virtual 3D model ofthe excised bone applied to the virtual 3D model of the patient'sabnormal anatomy to construct a hybrid model. Using this hybrid model,joints are identified where the bone graft will interface with (andhopefully join via bone growth) the adjacent residual bone. At thesejoints, depending upon various factors, such as surgeon preference, thesystem identifies bone graft plate locations and, for each plate, one ormore guides to facilitate correct placement and securing of the platesto the bone graft and residual bone.

Those skilled in the art are familiar with conventional mandible boneplates and, accordingly, a detailed discussion of general designs ofmandible bone plates has been omitted in furtherance of brevity. Whatthe present system and methods accomplish, unlike conventional systemsand methods, is the formation of patient-specific bone plates andplacement guides that account for the shape of both the residual boneand the bone graft. In particular, for each bone plate locationidentified (either automatically or manually), the system designed avirtual 3D bone plate and associated placement guide. Each virtual 3Dbone plate and guide model is overlaid with respect to the hybrid 3Dmodel (including bone graft and patient residual bone in theirreconstructed location) to ensure the underside of each virtual 3D boneplate and guide model is the negative of the underlying bone, whetherthat comprises the bone graft or the residual bone. In this manner, thevirtual 3D bone plate and guide model work together to ensure properplacement of the bone plate and corresponding engagement between thebone plate, bone graft, and residual bone. Exemplary mounting techniquesfor securing a bone plate to a bone graft and residual bone may include,without limitation, screws, dowels, and pins. In order to accommodateone or more of these mounting techniques or others, each virtual 3D boneplate and placement guide includes one or more through orifices. Afterthe design of each virtual 3D bone plate and guide is completed, thesystem generates and outputs machine code necessary for a rapidprototyping machine, CNC machine, or similar device to fabricate each 3Dbone plate and guide, which is followed by fabrication of the actualbone plate and guide.

Following from the above description and invention summaries, it shouldbe apparent to those of ordinary skill in the art that, while themethods and apparatuses herein described constitute exemplaryembodiments of the present invention, the invention contained herein isnot limited to this precise embodiment and that changes may be made tosuch embodiments without departing from the scope of the invention asdefined by the claims. Additionally, it is to be understood that theinvention is defined by the claims and it is not intended that anylimitations or elements describing the exemplary embodiments set forthherein are to be incorporated into the interpretation of any claimelement unless such limitation or element is explicitly stated.Likewise, it is to be understood that it is not necessary to meet any orall of the identified advantages or objects of the invention disclosedherein in order to fall within the scope of any claims, since theinvention is defined by the claims and since inherent and/or unforeseenadvantages of the present invention may exist even though they may nothave been explicitly discussed herein.

What is claimed is:
 1. A method of constructing a patient-specificorthopedic implant comprising: comparing a patient-specific abnormalbone model, derived from an actual anatomy of a patient's abnormal bone,with a reconstructed patient-specific bone model, also derived from theanatomy of the patient's bone, where the reconstructed patient-specificbone model reflects a normalized anatomy of the patient's bone using astatistical atlas, and where the patient-specific abnormal bone modelreflects an actual anatomy of the patient's bone including at least oneof a partial bone, a deformed bone, and a shattered bone, wherein thepatient-specific abnormal bone model comprises at least one of apatient-specific abnormal point cloud and a patient-specific abnormalbone surface model, and wherein the reconstructed patient-specific bonemodel comprises at least one of a reconstructed patient-specific pointcloud and a reconstructed patient-specific bone surface model;optimizing one or more parameters for a patient-specific orthopedicimplant to be mounted to the patient's abnormal bone using data outputfrom comparing the patient-specific abnormal bone model to thereconstructed patient-specific bone model; generating an electronicdesign file for the patient-specific orthopedic implant taking intoaccount the one or more parameters; comparing the patient-specificabnormal bone model to a normal atlas bone model to identify missingbone or deformed bone from the patient-specific abnormal bone model;and, localizing the missing bone or deformed bone onto the normal atlasbone model; wherein comparing the patient-specific abnormal bone modelto the normal atlas bone model to identify missing bone or deformed bonefrom the patient-specific abnormal bone model includes outputting atleast two lists of data, where the at least two lists of data include afirst list identifying the missing bone or the deformed bone, and asecond list identifying bone in common between the patient-specificabnormal bone model and the normal atlas bone model.
 2. The method ofclaim 1, wherein: the first list comprises vertices belonging to themissing bone or the deformed bone from the patient-specific abnormalbone model; and, the second list comprises vertices belonging to bone incommon between the patient-specific abnormal bone model and the normalatlas bone model.
 3. A method of constructing a patient-specificorthopedic implant comprising: comparing a patient-specific abnormalbone model, derived from an actual anatomy of a patient's abnormal bone,with a reconstructed patient-specific bone model to discern what bone inthe reconstructed patient-specific bone model is not present in thepatient-specific abnormal bone model, also derived from the anatomy ofthe patient's bone, where the reconstructed patient-specific bone modelreflects a normalized anatomy of the patient's bone using a statisticalatlas, and where the patient-specific abnormal bone model reflects anactual anatomy of the patient's bone including at least one of a partialbone, a deformed bone, and a shattered bone, wherein thepatient-specific abnormal bone model comprises at least one of apatient-specific abnormal point cloud and a patient-specific abnormalbone surface model, and wherein the reconstructed patient-specific bonemodel comprises at least one of a reconstructed patient-specific pointcloud and a reconstructed patient-specific bone surface model;optimizing one or more parameters for a patient-specific orthopedicimplant to be mounted to the patient's abnormal bone using data outputfrom comparing the patient-specific abnormal bone model to thereconstructed patient-specific bone model; generating an electronicdesign file for the patient-specific orthopedic implant taking intoaccount the one or more parameters, wherein: comparing thepatient-specific abnormal bone model with the reconstructedpatient-specific bone model includes discerning what bone in thereconstructed patient-specific bone model is present in thepatient-specific abnormal bone model; and, determining implant loci forthe patient-specific orthopedic implant using a normal atlas bone modeland results from discerning what bone is and is not present in thepatient-specific abnormal bone model.